ToK Essay Starter Activities
Brief ToK Essay Starter Activities: As the May 24 ToK Essay Titles are released teacher colleagues are planning how they will prepare students to write those essays I thought it would be useful to share a few resources that I have used with students in the early phases of the Essay process.
ToK Essay Activators
The ToK Essay Activators are questions that students can use at the beginning of their ToK Essay Planning Process. They are a way to start to understand the essay title. They’re the foundation of thinking.
The video explanation of this resource is linked here, and below.
A more detailed explanation of how to use these can be found here.
Link to FREE resources:
The "How to do the ToK Essay" video series.
Connect ToK Concepts to Knowledge Arguments.
The ToK Essay tests a number of fundamental skills, including:
The identification of ToK Concepts in the PT.
The extrapolation of ToK Concepts from the PT, and into knowledge issues.
The development of knowledge arguments (relating to the PT) based on the Knowledge Issues & concepts identified.
ToK Teachers can design a range of activities to help students develop and extend their skills in these areas. This could include familiarisation with the ToK Concepts (through games and drama). Here I present slightly more advanced skill development, the skill of connecting the ToK Concepts to Knowledge Issues.
The task is fairly obvious, but can produce quite developed / deep discussions amongst students. The students are asked to draw a line between the ToK Concept and the Knowledge Issue, and explain the connection.
You can swap out the Knowledge Issues to make them more focussed on specific Prescribed Titles, AoKs etc.
A more detailed explanation of the task, with related videos can be found at this link.
You can get a PDF copy of this graphic organiser at this link.
Other resources:
We have many other resources to help both teachers and students with the ToK Essay, and we will be publishing May 24 specific resources in the coming weeks. In the meantime you may find the following useful:
66+ Videos on writing ToK Essay, ToK Essay issues, RLS that can be used in ToK Essay etc.
80+ Blog Posts on the ToK Essay.
Lots of Student Specific Resources to help with you ToK Essay.
If you have questions about the ToK Essay, or suggestions for new content, I'd love to hear from you: Daniel@TokToday.com.
Stay Toktastic my friends,
Daniel, Bangkok, September 2023
Understanding Intuition in the Context of ToK
Intuition is often seen as a mysterious, yet common and powerful form of knowledge. It sits in a space between a form of knowledge and an emotional response. Notably, we base many of our vital decisions (romantic partners, jobs, etc) on intuition. However, the role of intuition in decision making isn't very well understood. When it comes to experience, intuition often doesn't seem to match up with reason-based ways of knowing.
How Intuition and Emotions Fit Into ToK
A Theory of Knowledge (ToK) that includes intuition must also consider whether emotions are a form of knowledge. Neglecting intuition (or other emotions) in ToK misses some of the most critical influences on what we know, and why we know it. Hence, it's important to discuss intuition as a form of knowledge in ToK.
AoK Natural Sciences: Defining Intuition
We kick off with AoK Natural Sciences. Volz and Zander (2009) define intuition as a non-conscious process influencing behaviour, which operates based on implicitly acquired knowledge and signals to higher processing areas in the conscious brain. This takes us straight to the realm of AoK Human Sciences, especially neuro-psychology.
Neuro-Psychology and Intuition in ToK
To see intuition as more than an elusive meta-reality, we can examine cognitive processing in the memory and attention systems. Intuition might be understood as a process of linking implicit memories to conscious and subconscious memory systems. This perspective on intuition leads us to several intriguing knowledge questions about physical sensory perception.
Voss & Paller’s research published in Nature Neuroscience in 2009, provides evidence that the retrieval of explicit and implicit memories involves distinct neural substrates and mechanisms.
Essentially the research shows that stimuli encoded and stored whilst attention was diverted elsewhere were remembered more strongly than stimuli which were directly encoded through volition. As such this research indicates that intuition is most probably a product of learned behaviour rather than an innate ‘sixth sense’. In ToK terms this means that intuitive knowledge is formed indirectly without the proactive volition of the knower - we’ll call this the “indirect learning hypothesis”.
Knowledge Questions about Sensory Perception in ToK
The 'indirect learning' hypothesis of intuition brings forth a range of interesting knowledge(ish) questions about physical sensory perception, such as:
Is evolved niche development the cause or consequence of the development of sense perception?
Why did visual perception become the primary human sense?
Have we 'lost' perceptual senses beyond those currently known?
Is the residual data from lost/declining senses now labelled as intuition?
If Intuition is based on neurological processes of perception and learning (albeit indirect learning) then we should be able to improve decision making which is apparently based on ‘intuition’. This is exactly what Wan et al (2012) demonstrated with the training of novices in the game of Shogi (Japanese Chess). They trained the novices for 15 weeks, whilst also monitoring neural activation through fMRI. Wan et al took ‘next-move’ knowledge as being indicative of the knowledge that we usually label as ‘intuitive knowledge’. They compared professional players with amateurs, and found that professionals had a significantly higher level of stimulation of the caudate nucleus, an area in the dorsal of the Basal Ganglia. The role of the caudate nucleus in voluntary motor functioning has long been known, we are now beginning to understand that it also has a role in spatial mnemonics – which is similar in aspect to muscle memory. It is clear that indirect learning is involved in muscle memory, and other sensory based memories, as such the neurological basis for intuition becomes established.
The Importance of Intuition in ToK
In ToK terms this means that intuitive knowledge forms perception, and more pertinently perspective. These ‘frameworks of knowledge’ are acquired / socially constructed (through communities of knowers). However, they have an empirical biological base. This draws into focus the question of free will vs determinism - To what extent are we free to acquire / produce knowledge independently through volition, and to what extent is the acquisition & production of knowledge bound by external determinants such as biological conditioning ? This is even more acute given that the learning that leads to intuition is involuntary and indirect - ie we don’t choose to do it, we don’t know we’re doing it, and we have little control over it !
The claim that intuition has a neurological basis should be of interest to ToK students because firstly it gives an empirical basis for knowledge without evidence. Secondly, it starts to bring ‘scientific evidence’ to the constraints on our knowledge world. It leads us into the idea that our knowledge frameworks are, to a degree, the product of the limited boundaries of our biology. As such this claim leads to the possibility of currently unspecified AoK’s, those which have possibly ‘declined’ / lost during human evolution.
Intuition's Neurological Basis and its Impact on ToK
n conclusion I come back to the power of intuition, it’s a form of knowledge that we rely upon to validate other other forms of knowledge, and sometimes to make important decisions. Neuroscience is increasingly showing us that intuition is actually a learned set of skills and knowledge. As such it should be possible for us to teach people to be more intuitive. This would lead to better, and faster, decision making. As such knowledge of intuition becomes both an individual and social good.
In conclusion, intuition is a powerful form of knowledge that we rely on to validate other forms of knowledge and make important decisions. Neuroscience is increasingly showing us that intuition is actually a learned set of skills and knowledge. Therefore, it should be possible to teach people to be more intuitive, leading to better and faster decision making. Thus, knowledge of intuition becomes both an individual and social good.
ToK Exhibition Preparation
If you're preparing your ToK Exhibition, or deciding which ToK Exhibition prompt to use, be sure to check out our range of ToK Exhibition e-books - ToK Exhibition prompts explained. They provide step-by-step ways of developing knowledge arguments for each ToK Exhibition prompt, along with examples of objects that you could use. You can pick up the e-book of all prompts explained, or get an e-book for just 5 of the prompts, and we even have e-books explaining just the individual most popular prompts - whichever best suits your needs.
We also have resources to help you with your ToK Essay, and coaching services offered here.
Stay TokTastic my friends,
Daniel, August 2023
Post-Structuralism and ToK
Why do we have to do ToK ?! I have heard a few DP students cry out in the past. “Why examine the construction of everything we have ever known when there’s cricket, ice-cream and cake ?” I have replied rather sarcastically. However, maybe those ToK resistant students were making a more philosophical point - maybe they were rejecting the essential structuralism underlying the Theory of Knowledge (ToK). Today on ToKToday we consider the post-structuralists!
Post-structuralism and ToK.
My last 2 blogs were on Structuralism - the core philosophical approach on which ToK is built. Today we’re going to talk about the criticisms of structuralism, and alternative approaches to thinking about knowledge. Structuralism emphasises underlying structures in determining meaning and knowledge. This has been criticised by thinkers like Jacques Derrida, Jacques Lacan, Michel Foucault, and Pierre Bourdieu, these writers moved beyond structuralism to what's often referred to as post-structuralism.
The Pillars of Post-structuralism and ToK: Derrida and Deconstruction
Derrida, a key figure in this movement, critiqued the structuralist focus on binary oppositions and stable structures. He introduced the concept of "deconstruction," challenging the idea that meaning could be fixed within a structure. Instead, he suggested that meaning was always deferred, in a constant play of signification. Derrida criticised structuralism's attempt to reach a final interpretation or an ultimate structure, asserting that such a task was impossible as every interpretation could be deconstructed further.
Jacques Lacan's Contribution to Post-structuralism and ToK
Lacan, a psychoanalyst, extended structuralism into the realm of the unconscious mind but also critiqued its limitations. While he used Saussure's linguistic model to understand the unconscious, he argued that structuralism failed to account for the complexity of human subjectivity. For Lacan, the subject's position within a structure was always fraught with inconsistencies and contradictions. This resulted from what he called the "Real," a dimension of experience that resists symbolisation and hence disrupts the symbolic structures of language.
Michel Foucault's Critique in the Light of Post-structuralism and ToK
Foucault's critique revolved around power relations and discourse. He rejected the idea of stable, universal structures, arguing that what appears as a structure is often a reflection of prevailing power relations. For Foucault, structures such as societal norms or discourses are historically contingent, shaped by power and subject to change. Therefore, structuralism's quest for universal structures was, in Foucault's view, misguided.
Pierre Bourdieu and the Dynamics of Post-structuralism and ToK
Bourdieu, a sociologist, criticised structuralism for its deterministic view of social structures. While acknowledging the influence of structures such as class, gender, or race, Bourdieu proposed the concept of "habitus" – a set of dispositions that individuals internalise from their social conditions but which also enable them to act and innovate. This was his way of reintroducing agency into the structuralist framework, arguing that individuals are not just passive products of structures but also agents capable of transforming them.
These criticisms point to common themes: the limitations of binary oppositions in structuralist thought, the neglect of power relations and historical contingency, and the downplaying of individual agency. Yet, despite these critiques, it's important to note that these thinkers built upon structuralist insights. Derrida's deconstruction relied on close readings of texts, Foucault's discursive structures were still structures, and Bourdieu's habitus was a way of mediating between individuals and structures.
Post-structuralism and ToK: A Conclusion
In essence, while post-structuralists critiqued structuralism, they also extended and transformed it, leading to a richer understanding of how structures shape, and are shaped by, our experiences and actions. It is through this dialectic of critique and development that knowledge advances, offering us increasingly refined lenses to interpret and engage with the world.
I hope that you enjoyed exploring the fascinating journey of post-structuralism and its impact on ToK !
For extra help with your ToK Essay or Exhibition, we have loads of resources available on from our student support page, including ToK coaching, written feedback and the ever-popular e-book, How to Write the ToK Essay in 6 Easy Steps.
If you’re doing your ToK Essay you may be interested in:
The Ebook : How to Write the ToK Essay in 6 Easy Steps
3 Tips for choosing your ToK Essay Title.
Scientific Anomalies in the production of knowledge.
If you’re writing your ToK Exhibition Commentary you may be interested in:
Linking the object to the Prompt,
ToK and Structuralism: A Study of Knowledge or Relationships?
Structuralism and ToK sounds complex, let's start with a straightforward question:
Is ToK (Theory of Knowledge) the study of knowledge or is it actually the study of the relationship between phenomena? We can explore this using some illustrative examples.
Understanding the World Through Structuralism
Using the masterful drawings by Dutch artist MC Escher, we can delve deeper into these relationships. Notice the blue and white-grey fish; Do they define each other? Could one exist without the other? Are the fish at all important, or is it the relationship between them which is vital?
Similarly, look at this other illustration. The color of the animal does not accord with a specific animal, so is the shape determined by the animals around it? This brings us to the idea of structuralism, where it's the relationships between things that define them rather than the things themselves. This concept forms the backbone of much of what we learn in ToK.
Some of the people who wrote the theory of knowledge believe that it is the relationship between things which forms knowledge rather than the knowledge itself. These people are called Structuralists, and their thinking informs much of what we learn in ToK. Therefore I think we need to look at structuralism in more detail.
The Birth and Expansion of Structuralism
Structuralism emerged in the early 20th century through the work of linguists like Ferdinand de Saussure and later expanded to other fields by scholars such as Claude Lévi-Strauss in anthropology and Louis Althusser in philosophy. Structuralism is a way of understanding how we interpret and construct meaning from our experiences, and therefore it is one way of explaining how we produce and acquire knowledge.
Understanding Structuralism: Relationships and Structures
At its core, structuralism proposes that our understanding of reality is not based on individual elements themselves, but rather on the relationships and structures that connect these elements. De Saussure argued that language, for instance, functions not due to the inherent meanings of words, but because of the differential network of relationships between them. For example, we understand the meaning of "day" because we understand its difference from "night"
Structuralism: Binary opposite relations.
In this perspective, meaning is constituted through binary oppositions and relations, implying that the essence of any element can only be comprehended in its relation to others within a given structure. This idea rejects the notion of intrinsic meaning, emphasising instead the collective structures that underpin our understanding and knowledge. As such knowledge does not have inherent meaning, but is relative and contestable.
Structuralism and ToK: Decoding the Interconnectedness
Applying this to ToK, the structuralist view suggests that these structures are not just external, but also internalised and form a crucial part of our cognitive apparatus. ToK Essay prompts often delve into the organisation & classification of knowledge, and many ToK Exhibition questions address structuralism.
Structuralism in Areas of Knowledge
In AoK The Arts, and Optional Themes Knowledge and Language, religion and indigenous societies, structuralist ideas, as proposed by Claude Lévi-Strauss, extend to the study of cultures. These ideas lead us to focus not on the knowledge itself, but on the underlying structures that define it. Strauss proposed that cultural phenomena like myths, rituals, and social norms can be understood as systems of symbolic communication operating on structural laws. The meanings of these phenomena emerge from their position within a larger, structured set of cultural relations.
As such we’re not focussing on the knowledge itself (e.g. knowledge as objects), but we’re looking at the underlying structures on which we place, or define knowledge. You will have come across this in many of your hexagon diploma studies. For example in Language A you may have looked at textual structures, story arcs or narrative structures. In Language B you will have looked at verb structures. In Individuals and Societies & Natural Sciences you may have looked at the methodology for carrying out research, and so on. The underlying structures on which knowledge is formed and defined are all around us.
Structuralism and Its Profound Impact
Structuralism’s core tenet, that meaning and knowledge are dependent on structures of relationships, has profoundly shaped our understanding of knowledge, language, culture, and cognition. It remains a critical tool for exploring how we generate and organise knowledge, offering deep insights into the relational nature of meaning and understanding.
Looking Ahead: Exploring Structuralism Further in ToK
In the next blog in this mini-series on Structuralism and ToK, I will look at how the work of some key structuralist writers has influenced our Theory of Knowledge. In the final blog in the series, I will address the critique of structuralism, and consider some post-structuralists.
For extra help with your ToK Essay or Exhibition, we have loads of resources available on from our student support page, including ToK coaching, written feedback and the ever-popular e-book, Every ToK Exhibition Prompt explained.
Stay toktastic my friends.
If you’re doing your ToK Essay you may be interested in:
The Ebook : How to Write the ToK Essay in 6 Easy Steps
3 Tips for choosing your ToK Essay Title.
Scientific Anomalies in the production of knowledge.
If you’re writing your ToK Exhibition Commentary you may be interested in:
Linking the object to the Prompt,
Priest's religious knowledge - do they believe in God?
Today's post can be used as an RLS for The Core Theme Knowledge & The Knower, and as RLS for AoK Human Sciences, and the Optional Theme Knowledge and Religion. It's about Priests who don't believe in God, and was the most popular post on my old ToK Blog (ToKTrump). There are obvious links with the role of Religious Knowledge in this research.
The Core Theme: Knowledge and The Knower is a very broad unit encompassing a wide range of knowledge questions. It can be a little unwieldy if not focussed onto some key knowledge questions, or a set of themes. I have slowly developed a sense that my student's most illuminating learning in this unit is firstly that knowledge is constructed rather than give, secondly that that process of construction is highly contextualised, and finally that it is not experienced as contextualised by the knower.
It's difficult to find the original study today, however I did find:
A review in The Atheist's Quarterly on JSTOR linked.
A summary on the website Why Evolution is True linked.
The world view of the knower is not experienced as contextualised, but is their "known world". We can draw upon Husserl's view of "Lebenswelt" or lived world here.
Why is The Priests who don't believe in God pertinent to ToK ?
Unstructured interviews of 5 non-believing priests carried out by Dennett & LaScola (2010) are a fascinating, and rare, insight into people who hold one set of beliefs, and yet live their lives by another set of beliefs. This dissonant state gives rise to a compelling set of insights for ToK. Whilst this example may not be 'typical' for most knowers, arguably it is in this somewhat extreme, contrast that we can uncover some of the processes of knowing that are experienced by all of us as knowers. Some of these implications include:
We can hold contradictory knowledge (and beliefs) at the same time.
Performativity of knowledge is both evidential and significant ( a behavioural element of knowledge).
Internal ethical justification of knowledge occurs when the knower is presented with contradictory or inimical knowledge/beliefs/values.
Even deeply held beliefs and values can change when the knower is challenged with opposing arguments/beliefs/values.
When deeply held beliefs/values are changed the knower may not change their public behaviours according to the newly held beliefs.
Beliefs & values (as forms of knowledge) can be known in many different ways by different knowers.
How to use this in ToK:
Core Theme: Knowledge & The Knower.
A quick skim through the KQs of the Core theme Knowledge & The Knower we can immediately see links to many KQs, particularly those dealing with the knower's knowledge in relation to others through interactions. I have allocated KQs to groups of students and asked them to use the research to explore their allocated KQ.
AoK Human Sciences.
The study can be relevant to all of the Hum Sci Knowledge Framework. Of particular interest to me is the link to perspectives and research methods. Specifically the validity vs reliability debate, and the value of extrapolation from a small (& we assume unrepresentative) sample.
Optional Theme - Knowledge & Religion.
Obviously there are a range of interesting KQs which could be explored using the Dennett & La Scola study. Of particular interest is the link between faith & religious beliefs, the role of culture's influence on religious beliefs, the relationship between reason and religious beliefs, etc.
For more ToK Lesson content for Knowledge and the Knower try this link.
For more ToK Lesson content on AoK Human Sciences try this link.
Conclusion.
The Dennett & LaScola research focuses on an atypical and unusual situation in knowledge. However, maybe it is in the strong contrasts found in the unusual cases that we can better understanding the framework and underlying processes of the knowledge held in all other cases.
If you would like more content like this (focussing on useful RLS), or have suggestions for further content please don't hesitate to contact me - Daniel@TokToday.com
Wishing you a great day!
Daniel, Lisbon, Jan 2023
Bibliography & References.
“Atheists Anonymous.” The Wilson Quarterly (1976-), vol. 34, no. 3, 2010, pp. 77–78. JSTOR, http://www.jstor.org/stable/41000971. Accessed 11 Jan. 2023.
whyevolutionistrue. “Dennett and LaScola Study of Nonbelieving Clergy.” Why Evolution Is True, 18 Mar. 2010, whyevolutionistrue.com/2010/03/18/dennett-and-lascola-study-on-nonbelieving-clergy/. Accessed 11 Jan. 2023.
Reflections on May 23 ToK Essay Session
I’ve been working with many students from all over the world with their May 23 ToK Essays. This post is partly a consolidation of reflection for me, but it should also be useful for other ToK teachers, and maybe for ToK students who are about to start their learning.
Why write reflections on M23 ToK Essay session ? Well, I’ve been working with many students from all over the world with their May 23 ToK Essays. This is the first time that I’ve worked with students who are not in my school, nor in my ToK class. And that's an interesting learning experience for me - because I don’t know what they’ve been taught, how they’ve been taught, what their teacher’s approach to ToK is, nor where the emphases and reference points are in their ToK knowledge. So, this post is partly a consolidation of reflection for me, but it should also be useful for other ToK teachers, and maybe for ToK students who are about to start their learning.
In essence, in this essay session I’ve gone from Goffman’s participant-observer to observer-participant.
So, what are the reflections on M23 ToK Essay Session (main learning points) ?:
1. Making or building the argument.
A significant issue for many of the students with whom I worked was that they lacked the skills, or knowledge, to build a ToK argument. And this causes many consequent issues. It leads to:
Problems with definitions, I heard a lot of questions such as - “How do I define this key term ? or that key term?” , “I can’t think of a definition for…,” etc
Developing claims or counterclaims, I got questions such as “I can;t think of a counterclaim for this”, or “how can I make this into a claim ?”
And finally problems with identifying RLS - “is this a good RLS for____?”
These 3 problems (definitions, claims/counterclaims, and RLS) come from not having the skills to build a knowledge argument. Let me, briefly, take each one in turn.
Definitions:
In this session we wrangled with definition such as “cannot be explained”, “replicability”, “bubbles” etc
Obviously students should not be using dictionary definitions, but some students are still using dictionary definitions. The definitions are often the basis of the whole essay, if you can’t develop the definitions then writing the essay is problematic.
There’s a mutually reciprocal relationship between developing the definitions of key terms and devising the knowledge claims / counterclaims.
There's also the problem of some students rewriting the key concepts - I particularly saw this with essay #2 - the vast majority of students I worked with doing this essay had redefined "cannot be explained" as "has not been explained". - there’s an important difference between the two,
Developing claims / counterclaims.
Some students seemed to be stuck in fairly rigid thinking when it came to devising claims / counterclaims. From some students there was a lack of flexibility / creativity in the interpretation of the title. This has made me go back to using more debate in the classroom with my own students, a sort of quasi application of De bono’s thinking hats.
Obviously, difficulty with developing claims / counterclaims can be partly due to a lack of clarity of definitions of key terms, or having dictionary based definitions of key terms.
Problems of identifying or applying RLS.
So this is the question “is X a good RLS for this claim ?”. Some students found it very difficult to identify appropriate RLS to demonstrate their knowledge claims.
Obviously if the claim is not fully understood then it's difficult to find RLS to demonstrate it.
It’s not about the RLS, it’s about the argument that’s being built. I believe that nearly ANY RLS can be used for ANY claim / counterclaim if the argument is well made. I’ll make a future video where we can take claims at random and match them to random RLS to show how any RLS can be used for any claim if you know how to make the argument.
further, and wider, reflections on M23 ToK Essay Session include:
2. Question Choice.
I think that essay # 6 on Methodology is by far the easiest prescribed title in this session, followed by essay # 1 on replicability. I won’t go into why I think they’re the easiest in this video, I made an earlier post & video about this linked here
Looking at all the data points that I have Essay #6 and Essay #1 are the LEAST popular titles in the session. I think that Essay #5 (visual representations) is probably the most popular.
Now, I certainly don’t think that we should tell students which essay to take - it’s meant to be their personal authentic reflection. However, Essay #6 & #1 have really straightforward structures, they don’t have multiple assumptions - they’re just straightforward. I don’t know how we get it over to students that they should consider the straightforward essays as little gifts from the examiners ! I find it particularly frustrating that we shouldn't direct students to questions, but those who need the most help often choose the hardest questions !
3. Use of the 12 ToK Concepts.
Most students with whom I worked were not intentionally using, or referring, to the 12 ToK Concepts. Some of the students didn't seem to be aware that there were 12 core ToK Concepts.
If we put them front & centre it helps to improve focus of the essay, Obviously any of the 12 concepts could be applied to any of the essays. So we need to get students to focus in on 2-3 specific concepts.
I try to get students to identify at least 2 concepts at the beginning of the devising process.
4. Questioning the title for Evaluation Points.
Some students didn’t realise that they can develop strong evaluation points by directly challenging the assumptions in the title.
The most obvious examples of this could be:
Essay #2: what can and cannot be explained may not be exclusive and consistent categories. That which can be explained in one context may not fall into cannot be explained in another context.
And, what we think can be explained today may become ‘cannot be explained’ as we gain new knowledge.
And I also saw this with Essay #4 (“so little knowledge so much power”), where some students didn’t understand that they are meant to challenge Russell’s assertion, that they need to make the argument that we either have a lot of knowledge, or little power, and all possible combinations thereof.
5. Impact of AI, esp ChatGPT.
I started to see content generated by AI coming through, much of this is easy to spot because the AI tends to write with sweeping introductory sections, and uses fairly vague generalisations with lots of hedging words. Obviously we also know it when we see a change in the tone of language used, or the sudden switch to American spelling and grammar.
There’s a lot to say about AI and the ToK Essay, probably in another video, but suffice to say here that currently AI can’t give us anything sufficiently precise to score well on a ToK essay without asking it precise and directed questions. The skills and knowledge required to frame those questions are at least as demanding as just writing the essay yourself. Currently it’s one of those situations in which it is more effort to use the AI than it is just to do it yourself. However, that may change in the coming years.
On 27th February 2023 IB gave guidance that AI generated content can be used, it should be cited just like any other secondary source.
6. Too much description of RLS. - link back to building the argument.
And finally, we come to our favourite old chestnut - description of the RLS. I still saw lots and lots of description of RLS which was largely unrelated to the PT, or knowledge claims being developed within the PT. This remains the most common problem at the Essay Draft stage in my experience, however I think that the latest Subject Report said that most of the essays submitted are now focussed on the PT, so teachers must be working hard to iron this out before final submission - well done teachers !
If you’re a ToK student , and you’re concerned about too much focus on the PT, pick up my e-book How to write the ToK Essay in 6 Easy Steps linked here. The book includes worked examples of how to make an overly descriptive essay more analytical.
So there we have it, reflections on M23 ToK essay session, a bit of learning from the May 23 Essay Session.
I’m looking forward to the Nov 23 titles coming out next week,n Have a great day, stay tok-tastic !
Daniel, Lisbon, March 2023
For other thoughts on ToK Essay:
Why do the best ToK Essays get Mediocre grades?
Unsubstantiated Assertions in The ToK Essay
ToK Exhibition - what do we know Feb 23?
In Feb 2023 IB posted 10 new Exemplars of ToK Exhibitions. I've closely analysed them to see if we can learn anything new from them, or if they further confirm things that we already know. I have summarised the main findings in the table linked below.
What we Know - Exhibition Feb 23
3 key findings:
Specificity of objects, and specificity of knowledge links between the objects & the IA prompt are key to doing well.
Three perspectives / arguments is key to developing the specificity required in the link and justification for inclusion of the object.
There's still some ambiguity as to whether objects symbolising knowledge / meaning / culture are allowed.
There are many other findings, clarifications and confirmations in the document - check it out.
If you have any thoughts, or corrections, I'd love to hear them in the comments below.
Have a great day,
Daniel, Lisbon, Feb 23
Further information on The ToK Exhibition can be found at:
Ethics & Technology.
When thinking about the possible ethical issues arising from technology in a ToK context I start from the perspective of ethics rather than technology. In doing so I will be asking 4 big questions:
What are the potential ethical issues arising from technology?
Where do these ethics and technology issues sit on a ‘good-bad’ continuum?
What are the implications of these ethics & technology issues for the construction and acquisition of knowledge?
How might these ethics & technology issues evolve in the future?
What are the potential ethical issues arising from technology?
We tend to think of ethical issues arising from technology in modern terms, ie issues arising from modern digital technologies. However there have been ethical issues arising from technology since humans first started to utilise technology. In the modern age such issues rose to greatest prominence during the first era of industrialisation in the 19th Century.
I will look at ethical issues arising from technology through 4 main lenses / perspectives. Each perspective has both positive and negative ethical consequences within it.
4 lenses that have ethical consequences arising from the application of technology:
Accessibility
Health, productivity and fulfilment.
Diversification - Homogenisation
Privacy - Autonomy - Control
Accessibility.
Positive ethical consequences of improved access afforded by technology.
Technology both increases and decreases accessibility across a wide range of fields, and in multiple ways. A starting point to think about this is that technology should increase our capacity to manipulate the environment. The outcomes of such manipulation in terms of accessibility will reflect the value basis of those in charge of the technology. Whether this is a negative, or positive, ethical outcome will also largely depend on the value basis of those making the judgement. Let’s take the application of technology in a mediaeval village as a real world example. The application of technology to crop production (eg ploughing, irrigation, crop rotation etc) increases the yield of production. Thus increasing access to food, and the time available to residents to access other ways to spend their time. There are subsequent consequent improved access improvements such as access to healthcare, culture, further technological innovation etc.
The most obvious improvements in access realised by modern technologies are access to knowledge acquisition and knowledge production. The development of public libraries, public education, and the internet have significantly improved the access that people have to knowledge acquisition. The development of universities, and more recently Web 2.0, significantly improves the access that people have to knowledge construction.
Schutz: The Social Distribution of Knowledge.
The positive ethical benefits of such improved access are wide ranging, and potentially profound. The expansion of human and civil rights seen in many areas of the world can be understood as being augmented by the improved access to knowledge realised by technology. In his article The Well- Informed Citizen: An Essay on the Social Distribution of Knowledge Alfred Schutz explains how improved access to knowledge changes the classification of an elite ‘expert’ group who are afforded the right to produce ‘socially approved’ knowledge (that is knowledge afforded prestige and influence). Technology allows more people to join this group, adding a more diverse set of perspectives to socially approved knowledge. Schulz uses the models of social identity theory from Social Psychology to understand the possibly liberating effects that wider knowledge access can have for individuals in breaking down in group-out group stratification. Further, Schulz explains how wider access to knowledge increases people’s opportunities to question taken for granted assumptions, and therefore increasing the potential to develop ‘better’ knowledge, ideas that have built upon, and evolved from that which is pre-existing.
Friedman: The World is Flat.
Schutz rather prophetically wrote his article in 1946, many of his ideas were updated in 2005 by Thomas Friedman in his book The World is Flat: A Brief History of the Twenty First Century. Friedman describes how technology (mainly Web 2.0 tech) has increased access to knowledge acquisition and construction which, in turn, has enabled those in developing countries to compete financially, technologically, and culturally with those in the developed world. Here we see the argument that access afforded by technology has significant positive ethical consequences.
Schutz rather prophetically wrote his article in 1946, many of his ideas were updated in 2005 by Thomas Friedman in his book The World is Flat: A Brief History of the Twenty First Century. Friedman describes how technology (mainly Web 2.0 tech) has increased access to knowledge acquisition and construction which, in turn, has enabled those in developing countries to compete financially, technologically, and culturally with those in the developed world. Here we see the argument that access afforded by technology has significant positive ethical consequences.
Negative ethical consequences of improved access afforded by technology.
The most common concern raised with the increased access that technology allows is applied to digital technologies - the digital divide. We will come to that later in this section, but let’s first take a step back and consider the wider knowledge implications of increased access to technology.
The argument for the positive ethical benefits of increased access to technology is essentially that more of things (knowledge & its consequences) is a good thing. However, this doesn’t account for the context within which this change occurs, specifically the values and purpose of the wider society. That increased access will have outcomes which are, arguably, largely shaped by the value structures and power relationships of the wider society within which they operate. Namely, the value structure of the wider society. If the value structure is one in which power is used to restrict the freedoms, or privacy, of the individual then this could be reflected in the wider access to knowledge afforded by technology. Not only does the mass populace have more access to knowledge, but powerful actors now have more access to the thoughts and behaviours of the population. We will come back to this in the section on privacy.
The concept of ‘more’ isn’t necessarily a positive ethical outcome, let’s go back to our mediaeval village. After the application of agricultural technology the people have more time to do things other than farming. However, if there’s a power structure which can direct how people spend their time (eg a Lord of the Manor) this extra time could be used for negative ethical outcomes such as warfare, environmental destruction etc.
The Digital Divide.
The digital divide refers to the unequal distribution of technology and internet access between different groups of people globally. The divide creates a gap between those who have access to the knowledge resources opened up by the internet and those who do not, leading to unequal opportunities for growth, development, and success. For example Northern Europe has an ‘internet penetration’ (people who have access to the internet) of 95% whilst Africa has an ‘internet penetration’ of approximately 40% (“List of countries by number of Internet users”)
The digital divide affects individuals from different regions, socio-economic backgrounds, age groups, and cultures. People who live in rural areas, low-income communities, and developing countries often lack access to technology and the internet. This limits their opportunities for education, job training, and accessing information, which can lead to lower wages and limited opportunities for social and economic mobility.
The digital divide also affects businesses, as those without access to technology may struggle to compete in the global market. Moreover, it perpetuates existing inequalities, such as gender and race, as marginalised communities are often the ones who lack access to technology and internet.
The Digital Divide is an example of technology widening the gap of power between those with and without access to the technology. This is a recurring theme that we will see arising from many different types of technology across history.
Health, productivity and fulfilment.
I am rather uncertain about including a section on the applied ethical effects of technology as I run into danger of describing the real world ethical effects of technology rather than focussing on the ethical effects of technology on knowledge acquisition, construction and interpretation. However, as this is somewhat a false division, and students need to draw upon real world examples for both ToK assessments, I’ll go ahead and include this section anyway.
At prima facie level the immediate effect of the application of technology in area of life should be to increase productivity - this applies equally to the production of knowledge as it does to the production of ceramic vases, cars or pizzas. This increase in productivity has significant ethical effects. There is a long tradition of writers (eg sociologists, artists, economists etc) describing the negative effects of technology in the workplace. There is a significant body of research on the alienation and deindividuation experienced in the industrialised workplace. In his paper on Technology and Human Relations Carleton Coone argues that increased application of technology requires a more pressing focus on designing for human relations if we are to avoid the negative effects of that technology (such as alienation). Students can review the paper (referenced below) for real world examples.
On the other hand, Jon Shepard, in his 1973 research in the oil industry, found that the application of technology did not inevitably lead to powerlessness and alienation. This finding was underpinned by two main processes (i) technology increases productivity, and therefore increases the free time that workers have to spend doing things that they find intrinsically fulfilling. (ii) that increased specialisation of roles afforded by technology can actually increase the autonomy and personal involvement that a worker has in their job.
The ethical consequences of medical technology.
The positive ethical benefits of the application of technology in medical sciences are both obvious, and seem to be indisputable. Students and teachers can draw from a vast range of real world examples including the development of vaccine technology, the application of medical imaging technology, the application of pharmaceutical technology etc. However, there are, arguably, some negative ethical consequences of the application of technology in the medical sciences. The application of technology to medical knowledge has the greatest possible ethical consequence - that of maintaining, prolonging or ending life itself. As such the responsibility of the practitioner, and the accountability of those practitioners, is brought to the forefront. The ethical demands upon practitioners is greatly increased by the increased efficacy of the technology.
Peter Singer (2000) identifies 5 areas in which the ethical considerations have greatly increased in recent years:
Quality of end of life care.
Tavistock Principles to improve medical error.
Prioritisation and access to resources.
Stem Cell Research.
E-health and bio-global ethics.
I include these here to demonstrate the nuanced range of ethics and technology issues arising from the application of technology in healthcare and medicine. If ToK students want to draw real life examples from these 5 areas they could follow the article referenced below, or search JSTOR for research related to the area of their choosing.
Ethical concerns of medical technology.
In his article Too Much Technology Bjorn Hofmann argues that the overuse of, and over-reliance on, technology is starting to have negative ethical consequences for the application of medical knowledge. He argues that the application of medical technology can actually be more harmful than positive, draws funding and resources away from functions which could have more positive effects, reduces efficiency, and leads to overdiagnosis. This last point is of direct relevance in ToK, and requires a little more examination.
Technology enables us to better identify disease and the causes of illness, further it helps us to better understand the interpolation of causal factors. As such it can allow us to develop new categories, and classifications, of medical conditions and illness. Therefore, the application of technology actually increases our construction of knowledge. Technology allows us to create more knowledge. An example of this from medical sciences is the recent development of a machine learning model which can predict rare diseases, even when these diseases aren’t represented in the data sample used (Singh).
Consequences
A consequence of improved medical technology is obviously increased human lifespan, which also has ethical consequences. The ethics of increasing human population on resource allocation and competition are well rehearsed, and easy to find sources for. As such I won’t detail them here. However, of direct relevance to ToK is the consequent increase in non-working (leisure) time, particularly the effects of the 4th industrial revolution with the application of artificial intelligence and machine learning. Jandrić, Petar, and Sarah Hayes (2020) look at the range of arguments whether the 4th industrial revolution will result in fewer jobs (“technological unemployment”) and more leisure time, or lead to the creation of a whole new range of jobs (as has happened in previous periods of technological innovation). Both scenarios will lead to new forms of knowledge production, acquisition, and interpretation as they further shape, and redefine, the human-machine interaction.
Further references to the work of Heidegger here will be useful for those who wish to follow this line of argument. There are ancillary ethics and technology issues in terms of the varied access to these forms of technology, and the consequent implications for power distribution, and life fulfilment.
Diversification - Homogenisation
Of more direct relevance to our Theory of Knowledge is the effect of technology on the type of knowledge produced, and the distribution of that knowledge. These arguments are most accessible to us in 2023 by considering the rise of digital technologies in the last 40 years, however the same principles apply to all previous forms of knowledge technology.
The oft cited promise during the mass adoption of the internet, and world wide web, in the mid 1990s was that many more people would have access to far more information faster and cheaper than ever before. Even taking into account the inequalities of The Digital Divide (discussed earlier in this article) this promise of mass access to mass information appeared to be accurate - this is the increased acquisition and interpretation of knowledge afforded by new technologies. The dawn of Web 2.0 in the early 2000s promised that many more people would be able to produce / construct knowledge through self publishing in written, visual, music etc form. Again, the rise of sites such as YouTube, Wordpress and TikTok would seem to have borne this promise out.
Concerns.
However, many writers have taken a more critical approach to this apparent diversification in the production, acquisition and interpretation of knowledge. Kumar Sashi (2011) uses a Gramscian framework to understand the operational processes and effects of digital technology on knowledge production and acquisition. His argument is that digital platforms operate like conventional markets, making the knower a consumer, and producer’s effectiveness is determined by their power. As such production of, and exposure to, agglomerates to the most powerful actors (knowers). The effect of power based agglomeration is the homogenisation of knowledge. Rather than horizontal demarcation of knowledge producers we have vertical integration and monopolisation of producers. This process is exacerbated by internal promotion systems (such as Google search algorithms, or YouTube’s “Like” algorithms). This is even further exacerbated by globalising forces which enable knowledge producers to transcend national boundaries, time zones and localised practices.
The ethical consequences of homogenisation.
The ethical consequences of such homogenisation of knowledge production and acquisition are equity and accessibility issues (as touched on earlier). There will be amplification of certain knowledge producers (and their content) beyond their real world organic functional niche. As such, the homogenisation of knowledge production and acquisition means that a narrower range of people’s content will be seen, causing an unequal distribution of influence in the social and political spheres - some people have more influence in political processes than others (links to the rise of populist movements can be made here, there is substantial literature on eg the rise of Trump, Bolsonaro, Black Lives Matters Movement etc. Students who take DP Global Politics will be able to make clear links to their units on social movements in politics).
Akin Unver, in the paper Politics of Automation, Attention and Engagement, argues that digital media platforms (eg Twitter, Tik Tok, etc) have become “political governance systems”. They allow both politicians and the electorate to bypass the traditional institutional gatekeepers such as the established media, institutions (eg Parliament), and opinion polling systems to communicate more directly with each other. Professor Unver explains the commodification of user attention, the rentier economic model of the private owned for profit platforms, and the effects of content control being moved beyond the traditional nation state. It is argued that this can lead to new forms of political ideology / techno-politico expression such as “cyber communism”, “networked feudalism”, and “Authoritarianism 2.0 and 3.0”. However, he argues that digital platforms are an imperfect democratic space which are playing the role of “saving democracy” in the new digital age. Their primary advantage over older forms of democracy is that they give users ‘sovereignty’ over their data and political voice.
Privacy - Autonomy - Control
There are well known, and widely discussed, concerns over the potential threats to the right to privacy raised by digital technology. The sharing of various forms of data with corporations and governments is an obvious consequence of digitalisation. However, similar concerns regarding privacy also apply to earlier forms of technology. An example could be the invention of the camera in the 19th century meant that people’s public behaviours could now be permanently recorded. As the discussions regarding privacy are well rehearsed elsewhere I won’t focus on them here, rather I will look at the effects of technology on individual autonomy, and on social control.
Most technology shapes the behaviours that individuals are undertaking, this is especially true in the workplace. Probably best exemplified by the advent of the production line during the 19th century industrial revolution in which people were often employed to carry out a small range of tasks with little or no latitude for variation. The increase in control over our environment afforded by technology somewhat paradoxically leads to a decrease in our freedom of choice of behaviours (eg hunter gatherers used a fire for warmth and protection at night, but now they are tied to staying near the fire as it provides so much more warmth and protection than any other resources available at that time). As such it can be argued that technology decreases our physical autonomy.
The ethics of the production line.
We can develop the physical autonomy argument further when we consider the role of control afforded by technology. The production line controls the pace of the individual’s work, and some corporations require their workers to wear tracking devices so that they know where they are at all times, etc . The examples are myriad. However, for our theory of knowledge we need to consider whether such control can also be applied to knowledge and thinking. In this we can refer back to the section re. Homogenisation and selection of messages in digital media earlier this article. We can apply the same models to earlier technologies - eg book publishers, religious organisations, even theatre producers have all been powerful gatekeepers during earlier stages of history. The important emphasis of the argument here is that technology amplifies their control.
Biotechnology and autonomy.
Further, the ethics and technology issues pertaining to autonomy and control are further exacerbated when we consider medical technology, particularly recent developments in bio-technologies. Some companies have been making employees wear fitness trackers for the past few years. The reasons given are the health benefits, but this also, undoubtedly encroaches on the individual’s privacy, and potentially their autonomy. There is now increased used of internal biological monitoring systems (eg monitoring cardio-vascular metrics), again the positive ethical benefits of health come with concerns over possible infringements of autonomy and control.
A further concern for autonomy and control arises from the increasing use of artificial intelligence and machine learning (see earlier post on AI). Artificial Intelligence is increasingly performing processes and functions that were previously within human control. Whilst the initial coding of the AI may determine how it performs those functions, machine learning enables the AI to learn, and adapt, it’s processes. As such, many argue that AI is reducing our autonomy over the functions that we give it (this is inherently the very point of AI). The ethical issues arising from AI include all of the above (autonomy, control, privacy, productivity, democratic, access).
Conclusions.
This article is an overview of some of the ethical issues potentially arising from the application of technology for ToK students studying the Knowledge & Technology optional unit. The article doesn’t intend to look in depth at the particular ethical issues, this would be better achieved by students who have chosen to investigate further those issues. In summary we have broadly described that whilst technology may bring positive ethical outcomes the nature of its application can also lead to some ethical concerns.
Daniel, Lisbon, Feb 2023
Other posts on technology in this series:
We need to talk about Pune India.
Bibliography
Coon, Carleton S. “Technology and Human Relations.” Proceedings of the American Academy of Arts and Sciences, vol. 75, no. 1, 1942, pp. 23–27. JSTOR, https://doi.org/10.2307/20023443. Accessed 7 Feb. 2023.
Friedman, Thomas L. The World Is Flat: A Brief History of the Twenty-First Century. New York, Farrar, Straus and Giroux, 2005.
Hofmann, Bjørn Morten. “Too Much Technology.” BMJ: British Medical Journal, vol. 350, 2015. JSTOR, https://www.jstor.org/stable/26518356. Accessed 7 Feb. 2023.
Jandrić, Petar, and Sarah Hayes. “Technological Unemployment and Its Educational Discontents.” The Digital Age and Its Discontents: Critical Reflections in Education, edited by Matteo Stocchetti, Helsinki University Press, 2020, pp. 161–82. JSTOR, https://doi.org/10.2307/j.ctv16c9hdw.13. Accessed 7 Feb. 2023.
Kass, Leon R. “Ageless Bodies, Happy Souls: Biotechnology and the Pursuit of Perfection.” The New Atlantis, no. 1, 2003, pp. 9–28. JSTOR, http://www.jstor.org/stable/43152849. Accessed 8 Feb. 2023.
KUMAR, SASHI. “Hegemony in Contemporary Culture and Media and the Need for a Counter Initiative.” Economic and Political Weekly, vol. 46, no. 51, 2011, pp. 38–43. JSTOR, http://www.jstor.org/stable/23065547. Accessed 7 Feb. 2023.
SCHÜTZ, ALFRED. “THE WELL-INFORMED CITIZEN: An Essay on the Social Distribution of Knowledge.” Social Research, vol. 13, no. 4, 1946, pp. 463–78. JSTOR, http://www.jstor.org/stable/40958880. Accessed 3 Feb. 2023.
Shepard, Jon M. “Technology, Division of Labor, and Alienation.” The Pacific Sociological Review, vol. 16, no. 1, 1973, pp. 61–88. JSTOR, https://doi.org/10.2307/1388654. Accessed 7 Feb. 2023.
Singh, Niharika. “Stanford Researchers Developed a Machine Learning Model Called POPDx to Predict Rare Diseases, Including Diseases That Aren’t Present in the Training Data.” MarkTechPost, 6 Feb. 2023, www.marktechpost.com/2023/02/06/stanford-researchers-developed-a-machine-learning-model-called-popdx-to-predict-rare-diseases-including-diseases-that-arent-present-in-the-training-data/. Accessed 7 Feb. 2023.
Singer, Peter A. “Recent Advances: Medical Ethics.” BMJ: British Medical Journal, vol. 321, no. 7256, 2000, pp. 282–85. JSTOR, http://www.jstor.org/stable/25225223. Accessed 7 Feb. 2023.
Unver, H. Akin. “DIGITAL CHALLENGES TO DEMOCRACY: POLITICS OF AUTOMATION, ATTENTION, AND ENGAGEMENT.” Journal of International Affairs, vol. 71, no. 1, 2017, pp. 127–46. JSTOR, https://www.jstor.org/stable/26494368. Accessed 8 Feb. 2023.
“List of countries by number of Internet users.” Wikipedia, https://en.wikipedia.org/wiki/List_of_countries_by_number_of_Internet_users. Accessed 3 February 2023.
Does my thermostat have feelings?
in other words : Does Artificial Intelligence 'Know' in the same way that humans know ?
Starting Points.
Artificial Intelligence (AI) has made remarkable progress in recent years, but it still remains an open question whether AI can know in a similar way to humans. The philosophical debate about AI and its capacity for knowledge has been ongoing for decades, with some arguing that AI can truly know, while others claim that AI lacks the necessary cognitive faculties.
The first person to use the phrase “Artificial Intelligence” was mathematician and computer scientist John McCarthy at The Dartmouth Conference of 1956.In 1979 McCarthy wrote an insightful, and prophetic, paper directly addressing the relationship between AI and human knowledge titled “Ascribing Mental Qualities to Machines”. This blog will draw upon McCarthy’s paper which seems more pertinent today than ever, whilst also offering alternative perspectives.
Ascribing Mental Qualities to Machines
John McCarthy's 1979 paper "Ascribing Mental Qualities to Machines" discusses the idea of machines having mental qualities, such as beliefs and desires. McCarthy argued that machines can have mental states and that it is possible to describe them using a system of mental attribution. He proposes that mental attribution should be based on the machine's behaviour and its goals, rather than its physical structure. The paper highlights the importance of considering the relationship between mental states and physical systems in artificial intelligence research.
McCarthy explains that the "beliefs" that a thermostat may be said to have relate to its operational functions ie to calculate whether it should switch the heater on to make a room warmer, or whether to switch the heater off to make a room colder. McCarthy further explains that the thermostat could not be said to have "beliefs about beliefs" ie, it doesn't have beliefs about what temperature the room should be, or whether it believes its beliefs. It certainly doesn't have beliefs outside of its operational (programmed) sphere such as who won the Battle of Waterloo.
Download Ascribing Mental Qualities to Machines
Some common criticisms of McCarthy’s position (as outlined in his 1979 paper) include the problems of defining mental states and attributing them to machines, the difficulty in creating a systematic method for mental attribution, and the concern that attributing mental states to machines may lead to anthropomorphizing them and losing sight of their true nature as mechanical systems.
Daniel Dennett’s critique of McCarthy
Daniel Dennett, philosopher and cognitive scientist, has written extensively on the topic of artificial intelligence and the idea of ascribing mental qualities to machines. Dennett has been critical of the idea that machines can have mental states, and he has put forward the argument that mental states are inherently subjective and cannot be reduced to purely physical or computational processes.
In his book "Consciousness Explained", Dennett argues that consciousness is an emergent property of complex systems, and that it is not possible to ascribe mental states to machines in the same way that we ascribe mental states to people. He proposes that the idea of mental states in machines is a form of anthropomorphism, and that it is more productive to focus on understanding the computational processes that underlie machine behaviour, rather than attributing mental qualities to them.
Dennett's views on this issue are significant because they challenge the idea that machines can have mental states and provide an alternative perspective on the relationship between mental states and physical systems in artificial intelligence.
Knowledge as Justified True Belief.
Questions on Artificial Intelligence in ToK inevitably start with the question of what knowledge is, and what it means to know. Many students are attracted to Gettier’s definition of knowledge as “Justified true belief”. Obviously, this definition has a number of implications for our studies in ToK. However, it's a useful place to start exploring the question of whether AI knows in the same way that humans know.
From a philosophical perspective, the concept of knowledge requires the existence of beliefs and understanding, as well as the ability to justify those beliefs through reasoning and evidence. Some argue that AI, with its vast amount of data and sophisticated algorithms, can possess knowledge in this sense. However, others argue that AI lacks the consciousness, introspection, and self-awareness that are necessary for true knowledge.
It's difficult to be rigorous about whether a machine really 'knows', 'thinks', etc., because we're hard put to define these things. We understand human mental processes only slightly better than a fish understands swimming.
John McCarthy "The Little Thoughts of Thinking Machines", Psychology Today, December 1983, pp. 46–49. Reprinted in Formalizing Common Sense: Papers By John McCarthy, 1990, ISBN 0893915351
Characteristics of Knowing
(from McCarthy’s article The little thoughts of thinking machines):
Intention
Tries
Likes & Dislikes
Self conscious.
Despite being written over 40 yrs ago McCarthy’s work is highly relevant today, and very easily accessible. (I strongly recommend both articles cited in this blog). His 4 characteristics of knowing provide a practicable starting point for ToK students who are exploring questions on AI and knowing. Whilst, prima facie, his characteristics may seem rather reductionist they are both pertinent to human and machine knowledge as they encompass goals, trial and error, reward and reflection.
Belief.
The difference between human belief and AI algorithms lies in the nature of their cognitive processes and the sources of their knowledge.
Human beliefs are shaped by a combination of personal experiences, cultural influences, emotions, and reasoning. They are based on a subjective understanding of the world and can change over time as new information becomes available.
In contrast, AI algorithms are based on objective rules and mathematical models that process data in a systematic and impartial way. They make decisions based on patterns in the data they have been trained on and do not have personal experiences or emotions that can influence their beliefs.
Furthermore, human beliefs often rely on intuition, speculation, and assumptions that may not be grounded in evidence, while AI algorithms can only make decisions based on the data they have been trained on and the rules they have been programmed with.
Machines as simple as thermostats can be said to have beliefs, and having beliefs seems to be a characteristic of most machines capable of problem solving performance. However, the machines mankind has so far found it useful to construct rarely have beliefs about beliefs, although such beliefs will be needed by computer programs that reason about what knowledge they lack and where to get it. Mental qualities peculiar to human-like motivational structures , such as love and hate, will not be required for intelligent behavior, but we could probably program computers to exhibit them if we wanted to, because our common sense notions about them translate readily into certain program and data structures. Still other mental qualities, e.g. humor and appreciation of beauty, seem much harder to model.
John McCarthy, "History of Lisp," 12 February 1979; republished at www-formal.stanford.edu.
Descartes on Machines & thinking.
René Descartes, French philosopher and mathematician, was one of the earliest thinkers to address the question of whether machines could think. Descartes believed that the mind and the body were separate entities, and that the mind was capable of thinking, reasoning, and having experiences, while the body was merely a physical machine.
In "Meditations on First Philosophy", Descartes argued that the mind was a non-physical substance that was distinct from the physical body. He believed that the mind was capable of independent thought and consciousness, and that it was not possible for a machine, no matter how sophisticated, to have these same qualities.
Descartes' views on this issue were influential in shaping the philosophical debates about the nature of consciousness and the relationship between mental states and physical systems. Although his views have been widely criticized and challenged in the centuries since they were first articulated, they remain an important part of the philosophical discourse on the topic of artificial intelligence and the possibility of machines having mental states.
In summary, the difference between human belief and AI algorithms lies in their sources of knowledge, their cognitive processes, and the nature of their decision-making. While AI algorithms can provide a highly accurate and objective approach to decision-making, they lack the subjective and personal aspects of human beliefs.
In terms of academic research, there is a growing body of evidence that AI can acquire knowledge through machine learning and other methods. For example, deep learning algorithms have been shown to successfully identify objects in images and perform other tasks that require knowledge of the world. However, there is still much work to be done in order to determine the limits of AI's knowledge acquisition and whether it can truly know in the same way as humans.
Consciousness.
Can artificial intelligence be said to have consciousness ?
Obviously, in order to answer the question whether AI can be said to have consciousness we need to consider different perspectives on what constitutes consciousness.
Many writers have attempted to describe the different characteristics which could be said to constitute consciousness. A useful writer in this area is David Chalmers (1995) who made the distinction between the easy problems of consciousness (characteristics which can be described by neurology and cognitive science), and “The hard problems” (more process based, or seemingly diaphanous characteristics);
the “easy problems” he describes include:
The focus of attention.
Assimilation of new knowledge into a knowledge system.
The communication of mental states.
Chalmers' 'easy problems' are a useful list for ToK students as they provide a model which can be easily used to compare AI with humans. We can identify both humans and AI executing, solving and displaying these processes/problems. However, are they 'the same' in both humans and AI ? Obviously to answer such a question we would have to consider what we mean by 'the same', but we could also introduce the notion of qualitative differences at this point. Those arguing for a more distinct difference between humans and AI might argue that whilst we can apply the same labels to processes in both entities (humans and AI) there is a qualitative difference in the ways in which those processes are realised and experienced. This (conveniently) takes us onto the issue of consciousness.
What is Consciousness ?
In this blog I only seek to answer the question on the nature of consciousness in order to help us to answer the question of whether AI knows in a similar way to the way in which humans know. A comprehensive discussion on what consciousness is would require writing a very long, and complicated, book - which is not the purpose of this blog !
Some of the main philosophical theories of human consciousness include:
Dualism: This theory, first proposed by René Descartes, holds that consciousness is a non-physical substance that exists separate from the physical body. According to dualism, the mind and body interact, but they are distinct entities.
Physicalism: This theory, as put forward by J.J.C. Smart, D.M. Armstrong, and Paul Churchland, proposes that consciousness is a byproduct of physical processes in the brain, and that there is no need to postulate a separate, non-physical substance to explain it. Physicalism is sometimes referred to as materialism or reductionism.
Idealism has been developed by writers including George Berkeley, Immanuel Kant, and Georg Wilhelm Friedrich Hegel. This theory holds that consciousness is the primary reality, and that everything else, including the physical world, is derived from consciousness.
Emergentism takes a more systems based approach to the problem of consciousness. Developed by writers such as Samuel Alexander, C.D. Broad, and J.C. Smart. This theory suggests that consciousness emerges from complex interactions between physical processes, but that once it emerges, it cannot be reduced to these underlying processes.
It's worth noting that these theories have been developed and modified over time by many philosophers, and that the list of proponents for each theory is not exhaustive.
How do these theories relate to Artificial Intelligence ?
Human & Machine Consciousness (David Gomez)
Dovid Gomez in his article Human & Machine Consciousness gives us some useful ways in which to approach the question of whether AI can achieve consciousness. The first distinction that Gomez makes is that clearly we can build AI which exhibits human conscious behaviours such as writing essays, driving cars and playing computer games. However, most commentators would agree that an external behaviour does not, in itself, constitute consciousness. For example, we often do things “without thinking”, ie we have not directed targeted consciousness towards our own behaviours.
Further, we can even build AI which is modelled on human consciousness, that will display conscious-like behaviours, and may even display evidence of self awareness. However, this does not necessarily mean that the AI is experiencing consciousness,
To paraphrase Mr Gomez:
A model of a river looks like a river, tells us about the form of a river, even about the functions of a river, but it is not necessarily wet.
Here we continue to see some of the distinctions between how and why questions, and necessary and sufficient conditions. To understand the function (how) of a river it is not necessary to make a river, to simulate a river, nor to even be like a river. Could this be the same with consciousness?
Further, Mr Gomez argues that until we understand the relationship between human consciousness & the physical world we will not be able to simulate consciousness in artificial systems. This challenge presents itself even before we attempt to demarcate the necessary and sufficient conditions of consciousness, and the functions and causes of consciousness.
In many ways we could argue that Mr Gomez is drawing upon a more physicalist tradition in thinking about consciousness. He posits AI as outcomes which are, to a degree, dependent upon the physical processes and outcomes of human consciousness.
Free Will.
Consciousness, for most people, requires a degree of free will. Or at least free will constitutes a desirable component of consciousness. However, Nahmias et al in their paper Can AI have Free Will ? challenge this apparent link between consciousness and free will. Both consciousness and free will are context dependent, we could easily be in a state in which we are highly aware (conscious) of our lack of free will etc. As such, they argue, that free will is not a necessary, nor even sufficient, condition for AI to be said to be self-aware, or conscious. This argument is a strong counterargument against the “AI achieves Singularity and takes over the world” scenarios so prevalent in popular fiction.
In this (very brief) discussion of free will and AI we raise some of the issues arising from the Dualist and Idealist theories of consciousness. If consciousness is tenuously related to the physical structures within which it occurs, or from whence it is produced, then it could be expressed as being ‘beyond’ the limitations of those structures. This discussion brings into sharp focus the question of the purpose of consciousness, and free will, in an AI system.
Imagination.
Most people view imagination as being central to our human consciousness, and often posited as a necessary condition of our free will. Critics of the notion that AI can achieve consciousness often cite the difficulty of simulating imagination as evidence that AI cannot become conscious. Hilary McLellan, in her work AI & Imagination, takes a Systems Theory approach rather than a functionalist atomistic approach to the issue of imagination. In systems theory the emphasis is placed on the processes and interactions between the components of the system rather than on the components themselves. In these processes and interactions we see the product or output. The processes work in a synergistic manner in which the outcomes are greater than the sum of the products of the individual components. As such the product of the AI is greater than the sum of the outcomes of the individual units of coding.
Ms McLellan situates AI in the symbiotic relationship between culture and technology, culture changes technology and technology changes culture. In such an environment adaptation and evolution have low context relevancy (adaptation happens as context changes), however stability has very high context relevancy (the organism, or machine, can only remain stable if the context remains stable). This observation helps us to start to answer the question why AI would need imagination ?, and why might AI need imagination to attain consciousness ?
In evolutionary psychology imagination is often seen to be a survival function allowing us to predict threats, prepare for, and take actions to avoid such threats. If we see imagination as a necessary condition for AI to be said to be conscious we will need to consider whether AI has the same needs for threat protection ?
AI, Context & Consciousness.
Both Heidegger and Dreyfuss discussed the importance of context when considering whether AI knows in a similar way to humans. Heidegger, in explaining the Daesin, described how we achieve many things because of the way in which the world works rather than because of our knowledge of how things explicitly work. For example, we can achieve flying from one country to another by getting on a plane, we don’t actually have to know how a jet engine works to achieve this goal.
All of our human behaviours are highly context specific, we are consciously aware, and subconsciously sense, the immense range of factors constituting context. Dreyfuss called this immensely complex context “background”. Both writers argued that it is very difficult, nigh impossible, to code for / artificially simulate context. Heidegger argued that this difficulty is compounded because we have very limited understanding of our human contexts, the influences on, and experiences of those contexts.
Dreyfuss further developed Heidegger’s Daesin in his discussion on the unconscious mental processes which give us a sense of context, and in turn allow us to be consciously aware of our position and intention in context. Contextual understanding and contextually appropriate conscious thought and behaviour requires the corralling of a seemingly infinite number of facts. We have to select this huge number of facts from an even greater number of facts, in order to make correct selections we need rules about contextual based selection. These rules are, in turn, underpinned with even more facts about the selection rules. As such we see a mutually inclusive relationship between facts, rules, selection and context. Both writers argued that such endless possibilities are nigh impossible to code for.
Critics of Heidegger and Dreyfuss level two key critiques at this argument. Firstly, AI is only required to operate within a very narrow band / confines of contexts. AI doesn’t need to understand the wide range of human contexts that a human may encounter. AI which is developed to identify a particular type of fruit has a narrow band of contexts, with very limited variation.
Creativity.
AI can simulate certain aspects of human creativity, but it cannot truly replicate the full extent of human creativity. Human creativity involves the ability to generate novel and original ideas, to make connections between seemingly disparate concepts, and to imagine new possibilities. While AI can generate novel outputs based on patterns it has learned from data, it still lacks the ability to truly understand the context and meaning behind its outputs, and to experience the emotions and intuition that drive human creativity.
Additionally, AI algorithms are limited by the data and parameters they are trained on, and they may generate outputs that are not truly creative or original. In this sense, AI can only simulate certain aspects of human creativity and cannot fully replicate the richness and complexity of human imagination.
There have been some fascinating attempts to develop AI that has archetypical creative behaviours. At a phenotypical level these involve designing the AI to write music or paint in the style of pre-existing human artists.
Daddy's Car is an AI devised and produced song written in the style of the Beatles.
Art created by the AI Dall E2. I asked the AI to create "an oil painting in the style of Lucian Freud of a man reading a book in a library"
However, the field of AI and creativity is an active area of research, and AI is being developed to perform increasingly complex tasks related to creativity, such as generating art and music, or creating new designs and products. As AI continues to evolve, it is possible that it may one day be able to simulate human creativity in more sophisticated ways. But for now, human creativity remains a unique and valuable aspect of human cognition that cannot be fully replicated by AI.
Conclusion
Overall, the question of whether AI can know in a similar way to humans is still a matter of ongoing debate and research. While AI has made great strides in recent years, there is still much that is unknown about its capacity for knowledge and understanding. As the field of AI continues to evolve and advance, it is likely that we will gain a better understanding of the limits of AI's knowledge and its potential for truly knowing in the same way as humans.
Most crucially for ToK Students, we could argue that as we develop AI's capacity and abilities our definitions, or labels, of what it is doing will develop. We will develop labels beyond "machine learning", "algorithm" etc to describe new and richer processes that AI is capable of. In turn we may also develop new labels for human ways of knowing. As such this very question ("Can AI know in the same way as humans") will develop and change in the future.
Daniel, Lisbon, February 2023
Other posts / videos on Technology:
We need to talk about Pune India.
How does technology change our pursuit of knowledge?
Bibliography
Brandhorst, Kurt. Descartes’ Meditations on First Philosophy. Bloomington, Ind., Indiana University Press, 2010.
Chalmers, David. 1995. “Facing up to the Problem of Consciousness.” Journal of Consciousness Studies 2(3): 200-219.
Dennett, Daniel Clement, and Paul Weiner. Consciousness Explained. Little, Brown and Company, 2007.
Gamez, David. “Machine Consciousness.” Human and Machine Consciousness, 1st ed., Open Book Publishers, 2018, pp. 135–48. JSTOR, http://www.jstor.org/stable/j.ctv8j3zv.14. Accessed 31 Jan. 2023.
McCarthy, John (1979). Ascribing mental qualities to machines. In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence. Humanities Press.
"The Little Thoughts of Thinking Machines", Psychology Today, December 1983, pp. 46–49. Reprinted in Formalizing Common Sense: Papers By John McCarthy, 1990, ISBN 0893915351"The Little Thoughts of Thinking Machines", Psychology Today, December 1983, pp. 46–49. Reprinted in Formalizing Common Sense: Papers By John McCarthy, 1990, ISBN 0893915351
Mccarthy, John. “The Little Thoughts of Thinking Machines.” Psychology Today, 1983, pp. 46–49.
Nahmias, Eddy, et al. “When Do Robots Have Free Will?: Exploring the Relationships between (Attributions of) Consciousness and Free Will.” Free Will, Causality, and Neuroscience, edited by Bernard Feltz et al., vol. 338, Brill, 2020, pp. 57–80. JSTOR, http://www.jstor.org/stable/10.1163/j.ctvrxk31x.8. Accessed 31 Jan. 2023.
Preston, Beth. “Heidegger and Artificial Intelligence.” Philosophy and Phenomenological Research, vol. 53, no. 1, 1993, pp. 43–69. JSTOR, https://doi.org/10.2307/2108053. Accessed 31 Jan. 2023.
How does technology change our pursuit of knowledge ?
The question how does technology change, help or hinder our pursuit of knowledge will be approached by arguments that technology helps in the pursuit of knowledge, then arguments that technology hinders the pursuit of knowledge, and finally (and probably most importantly) the wider implications of the arguments & question.
The exploration of many of the KQs in ToK Optional Theme Knowledge and Technology could start by looking at different ways to define technology, I have tried to do this in my earlier blog post “What is Technology ?” - if you are yet to read that you may want to jump back to check that out.
A note on complexity and simplification.
Anyone undertaking even cursory reading into the technology and knowledge space will quickly be confronted with the word complexity. Virtually all writers agree that a contemporary understanding of the role of technology is becoming ever more complicated, and technology itself is only increasing that complexity. Whilst I do not wish to understate both complexity, and its’ importance, I don’t see it as being a useful starting point for ToK teachers in our work with DP students - it may be our eventual destination, but I think it important to explore some of the underlying processes and phenomena first in order to give a better sense of that complexity. Obviously such parsing can lead to a (justifiable) charge of reductionism, but there is an educational reason for such reductionism.
Technology helps in the pursuit of knowledge.
Technology as a tool.
If we start from the premise that technology is created to solve problems / meet needs then, quite obviously, technology helps in the pursuit of knowledge by giving us new ways to acquire knowledge, reveals new knowledge to us, and allows us to synthesis pre-existing knowledge into new knowledge. This is the fairly conventional, near orthodox, perspective on the role of technology in the construction of knowledge. It’s widely found in the ToK literature, and as such I will not spend too much time on this argument here.
The argument that physical artefacts created to solve problems increase our ability to pursue, acquire, construct (and discover) new knowledge appears to be fairly indisputable. It is, as I will show, highly disputable, but we need to go through the tech as a good tool argument first.
Technological innovation in terms of physical artefacts have always given us access to previously hidden / inaccessible knowledge. Appropriate examples are near inexhaustible, a few examples include: the map allowed for better navigation, the compass allowed for even better navigation, powered flight allowed for faster travel, and the placement of satellites in space, GPS brings all of these technological innovations to give us the knowledge of exactly where we are on the globe at any moment in time. The same arguments can be built for any number of physical technology eg microscopes, printing presses, the crossbow, x-rays etc
The “Tech as tool” argument becomes a little more interesting when we consider the technology as the knowledge which has led to the physical artefacts. For example the knowledge of geometry and algebra which enabled the map to be made, the knowledge of maps and physics which led to the compass, the knowledge of gaseous exchange and displacement which led to the rocket which deployed the satellite etc. This is interesting for two main reasons:
Tech as tool knowledge: causation issues.
Prima facie the cause of technology appears obvious - we create it to fulfil a need or solve a problem, this is often called the Functional explanation. We could ask why we decided to create a specific technology to fulfil a particular need rather than another form of technology, the answer to which is usefulness. This argument is well advanced by James Woodward in his paper A Functional Account of Causation; or, A Defense of the Legitimacy of Causal Thinking by Reference to the Only Standard That Matters—Usefulness (Woodward, 2004). As such we have a way in which Technology helps the pursuit of knowledge, namely in its usefulness to that pursuit of knowledge. Through processes of selection and application the technology is found to be the most useful way in which to pursue (as an umbrella term) knowledge.
The concerns with, and challenges to, the functional explanation arise from the causal relationship between the knowledge, need and technology. Again, a prima facie explanation posits the need as existing first, and thus causing the technology. However the relationship between the knowledge being pursued and the need is not clear - with many technologies we were aware of the knowledge before we had the technology to confirm it (eg we knew the moon existed before we could get there etc) - so not all needs can are uniform, neither are they uniform in their operation.
Further, much technological innovation reveals new knowledge when we apply it (for example we weren’t aware of spike proteins on viruses until we had electro-scanning microscopes) as such the need didn’t exist for the technology to be created. Therefore need (nor function, nor usefulness) do not seem to be necessary, nor even sufficient, conditions for the creation of new technology. If we accept this premise we could build an argument that technology neither helps nor hinders in the pursuit of knowledge, but rather has a more haphazard, unpredictable, directionless effect.
The causes of technological innovation are an issue for us if we hope to answer the help/hinder question - we will need to return to them in more detail when we look at the hinder side of this question.
“Hold up Daniel, you’re taking a very serial, near uni-directional view, of the relationship between technology and the pursuit of knowledge. It’s more nuanced and bi-directional than that.” I hear you cry, and you would be correct. So, let’s look in more detail at the processes of knowledge transfer which could give rise to new technologies.
Tech as tool for knowledge transfer.
Knowledge transfer is the process by which knowledge is transferred from one sphere to another, in ToK we could think of it in terms of transfer across concepts, theories, applications, disciplines, themes or Areas of Knowledge. It is not difficult to construct the fairly conventional argument that technological innovation in one area causes knowledge transfer to another area, which leads to subsequent technological innovation in that area. We often see examples drawn from the US space programme citing such processes. With application to the question regarding the role of technology in our pursuit of knowledge we can consider whether such technology transfer is necessarily causal in the creation of the new technology, whether the need for the new knowledge existed before the transfer (“The Russians took a pencil” story could be much cited here), and whether the transfer actually diminishes the pursuit of other forms of new knowledge in the second area - an issue to which we will return later in this piece.
Examples of such technology transfer that Diploma students may come across in their studies could include the development of fMRI in Natural Sciences led to new knowledge in Bio-Psychology in the Human Sciences, the development of new computer visualisation capability led to new techniques for designing objects and buildings, the development of social psychology in Human Sciences led to new models of epidemiology in The Natural Sciences.
Tech as Knowledge - Helps the Pursuit of Knowledge.
If we approach technology as a knowledge framework, or knowledge which gives rise to artefacts (tools) we start to develop some new ways in which we can argue that technology ‘helps’ the pursuit of knowledge.
A useful starting point for this approach is in the values, methods and purpose of the European Enlightenment (1600s+). At the core of the Enlightenment is a rationalist approach to thinking, and knowledge production / acquisition. That which is thought to be rational was often labelled as ‘useful’ knowledge - useful in that it allowed for greater control of (“instrumentality”) of the environment.
Underpinning the enlightenment is a significant increase in both the overall ‘amount’ of knowledge, and the ‘accessibility’ that people had to that knowledge. We see this at both the level of artefact (eg printing press and book) and at the institutional level (eg universities and public libraries). As such, we can argue that the knowledge framework of the enlightenment, as a technology for organising the world, significantly improved our pursuit of knowledge in terms of the aggregate production of knowledge and access to that knowledge.
However, this argument can be further developed in terms of the role of ‘useful knowledge’ in control (which further helps the pursuit of knowledge). In his article “The Intellectual Origins of Modern Economic Growth.” Mokyr, Joel argues that technology is the application of useful knowledge to control the unexpected (e.g. weather, natural disasters, and social behaviours caused by faith etc).
As such, Mokyr argues that ‘useful knowledge’ both produces technology and is sustained by technology, and could be seen as a form of technology in itself (similar to Heidegger’s ‘essence’). The processes by which useful knowledge affects this are (i) producing cultural cohesion through technological dispersion of knowledge, and (ii) allowing specialisation, professionalisation and expertisation. Both factors significantly increases the material wealth of a society, which further enables technological innovation. Here we see a worked through model of the “Some Knowledge is Tech” approach.
However, in evaluation Charles Gillespie in his book Science and Polity in France at the end of the Old Regime argues that the link between knowledge and technology is at best tenuous, and often knowledge lags behind technological innovation. This argument is taken further by Nathan Rosenberg and Derek Price argue that technology causes knowledge. In order to make this argument we need to separate out formalised, validated, knowledge (such as academic and theoretical knowledge) from applied, pragmatic knowledge. In doing so, we can argue that practical innovation of technology gives rise to new knowledge, and as such further helps our pursuit of knowledge
Rationalism & Specialism.
The premise of Nicholas Maxwell’s book Science, Reason, Knowledge and Wisdom: A Critique of Specialism.” provides us with a strong framework for the claim that Technology is a form of knowledge that helps with the pursuit of further knowledge. Maxwell argues that the function of universalism (ie the reason why we have knowledge) is to answer 4 questions:
1. What kind of world is this ?
2. How do we fit into the world, & how did we come to be ?
3. What is of most value in life, and how is it to be achieved ?
4. How can we help to develop a better human world ?
Maxwell argues that rationalism is a specific form of knowledge which enables specialisation in order to answer those 4 universal questions. These 4 questions are broken down into myriad smaller sub-questions helping us to form smaller solutions to the big questions. Specialisation of knowledge is the academic disciplines which give us subjects, leading to larger groupings as Areas of Knowledge.
Maxwell’s work (built on Popper, amongst others) provides us with a framework for both the Tech as Tool and the Most Knowledge is Tech approach in answering the question how does technology change our pursuit of knowledge ? In précis he is arguing that technology helps us in our pursuit of knowledge as it is the applied form of specialised rational thinking serving to answer the big questions of universalism. In this claim the 4 universal questions constitute the fundamental cause of technology, subject disciplines are the knowledge solutions of those questions, and reason and rationality are the enabling means of the subject disciplines.
For Maxwell ‘intellectual inquiry’ (ie our pursuit of knowledge) is a tool, as such it constitutes technology in itself. He states:
“Intellectual Inquiry is our servant, not our master. It is not in itself any kind of authority or oracle”.
Nicholas Maxwell
Tech as Tool - Hinders the Pursuit of knowledge.
The “Tech as Tool'' approach focuses on the physical artefacts of technology as constituting the technology itself. At first glance it would seem very hard to construct an argument that an artefact that gives us greater capacity to manipulate our environment would, also in turn, somehow hinder our pursuit of knowledge. It seems to be obvious that greater control of our environment would enable improved discovery / construction / acquisition of knowledge.
However the argument that Technology (as a tool) hinders our pursuit of knowledge can be developed on the basis of:
Alienation
Selectivity, Amplification and The long tail.
Homogenisation of knowledge.
Students may be very tempted to focus on the effects of technology on humans (such as reduced attention span, narrowing of interests, mental health etc). If these effects are commented upon it is important that they are explicitly linked to the “pursuit of knowledge”. The knowledge question is clearly focussed on the pursuit of knowledge, as such human effects of technology will need to make the association between psychological effects and the pursuit of knowledge. Nicholas Carr’s 2020 book The Shallows may be of use for students who wish to make this link.
There is obviously significant overlap with the Tech as knowledge approach here, which we will further develop in the next section.
Alienation:
The estrangement of individuals from themselves and others; a feeling of normlessness and powerlessness caused by separation and isolation from an individual’s sense of self, society, and work.
Open Education Sociology Dictionary
Many Human Scientists and Philosophers have written about ‘alienation’ as a product of human interaction with technology, especially relating to production in the workforce. Of particular note here is Robert Blauner’s 1964 account of working on the Ford production line (Peterson 1965).
It could be argued that this process of separation of the individual from their themselves, their wider knowledge community hinders the pursuit of knowledge. The process of alienation (caused by technology) arguably reduces the knowledge that may be acquired / constructed in collaboration with others as we are put into ‘silos’ by technology. Further, it could be argued that the feelings of powerlessness and estrangement caused by the technology reduce the individual’s self efficacy for the pursuit of knowledge. In this scenario individuals feel less confident, and intrinsically less interested, in new knowledge, or the creation of new knowledge.
Selectivity, Amplification and “the Long Tail”.
It is easier for us to understand the processes of selectivity, amplification, and ‘the long tail’ in modern digital technologies than in antecedent technologies, but the same processes apply to all technologies. Let’s take each process in turn, and apply it to the pursuit of knowledge.
Selectivity - technology generally selects both the knower and the knowledge to which they are exposed (eg the knower has to have access to the internet, algorithms will then select knowledge to which the knower is exposed). This process of selectivity hinders our pursuit of knowledge because it reduces the range of diverse knowledge to which we may be exposed, it reinforces the power of the select knowledge to which we are exposed. This is obvious with the modern internet, but the same processes are in play with the establishment of public libraries, the printing press, factories, even the crop rotation system and early Mesopotamia farming processes. In every implementation of technology we increase our control of the environment, consequently reducing our exposure to alternative environmental pressures. As such we reduce our exposure to alternative knowledge sources - these knowledge sources could have been previously unwanted, unpredictable, or thought to be unhelpful.
Amplification - Once selectivity of knowledge occurs the subsequent amplification of the knowledge that we have been exposed to occurs. We generally don’t experience our knowledge world in deficit or lacking, we experience it as ful. As such, if we have been exposed to only a limited range of available knowledge we will ‘amplify’ that to which we have been exposed to become representative of ‘all knowledge’. This process is evident in contemporary social media, but the processes also apply to all antecedent technology (Marx described it as the knowledge function of capitalism – calling it “Commodity Fetishism”).
Much recent political research (AoK Human Sciences) has been done on the role of selectivity and amplification in the rise of populist political movements in Europe and North America. Valentino et al (2013) discuss how selectivity in both pursuit of, and exposure to, knowledge on the internet both causes, and is amplified by, anxiety. Technology causes anxiety (alienation effects), and in turn this causes The Knower to find knowledge which corroborates their viewpoint in order to reduce their anxiety. Arguably, this hinders our pursuit of knowledge as further reducing the range of diversity of knowledge to which we are exposed, and augmenting the knowledge perspectives already held. This leads to a process of homogenisation of knowledge.
The Long Tail
The Long Tail is the theory that the internet offers us a far wider range of knowledge sources (it’s usually applied to business, so those knowledge sources are usually ‘products’ in the literature), and therefore we are able to specialise in niches. The theory would, therefore, probably support the claim that technology helps our pursuit of knowledge.
However, many recent studies have shown that the ‘long tail’ is not actually happening. What researchers are seeing (eg Netessine, Serguei. “Why Tom Cruise Is Still Bankable: Debunking the Long Tail.”) is that faced with a wide choice of knowledge sources we are more likely to fall back on what we know to be safe. This leads to further amplification of well known knowledge sources, and a reduction of exposure to lesser known alternative knowledge sources. This further supports the argument that technology hinders our pursuit of knowledge.
Tech as Knowledge - Hinders the pursuit of knowledge.
Beyond the arguments of the physical attributes hindering our pursuit of knowledge we can look at the KQ from the perspective that understanding technology is a form of knowledge that in turn inhibits the further pursuit of knowledge.
In this we will draw upon two main areas of philosophy:
(i) Aristotle’s 4 causes of technology
(ii) Martin Heidegger’s Question about Technology.
However, of course, ToK is not a Philosophy course, so we’ll just use these two philosphy sources as ‘underlying’ frameworks in order to unpack the knowledge question.
The starting point with this approach is that technology is a form of knowledge, and some (or all) knowledge is a form of technology. This argument is developed in the first blog & video in this series What is Technology? linked here.
How the causes of technology change / hinder our pursuit of knowledge.
Purpose.
We often find that technology is not used for the original purpose for which it was created. For example steam trains were invented to transport coal from the mine to the seaport, were adapted to carry humans, the world wide web was created to locate tanks on a battlefield, the necktie was created to fasten the upper seam of a shirt together etc.
The question arising from this re-purposing of technology is whether the need existed before the repurposing, and the new technology merely fulfills the need. Or, conversley did the new technology ‘create’ a new need which it then fulfilled ? This question is not central to our KQ (How does Tech change our pursuit of knowledge?).
The central concern arising here is whether the new technology (or knowledge) in the process of repurposing has limited our pursuit of knowledge in the area to which the technology has been repurposed ? Or has the repurposed technology led to new questions which further change our pursuit of knowledge ? For example, if the world wide web had not been created for military purposes, and then repurposed as the internet would we have found alternative technologies for sharing and creating knowledge amongst large groups of people ? Would these new technologies have avoided some of the negative consequences of mass group based interaction that we see with the internet ? And, most importantly - did the invention of the internet constrain the pursuit of alternative technologies ?
Potentiality.
The conventional tech as tool approach puts forward a fairly straightforward view that a need exists, knowledge is garnered to create a solution to that need, and that solution is new technology. This new technology in turn gives us access to new knowledge. As such, the argument goes that technology helps our pursuit of knowledge.
However, one of the problems with this argument is that it implies a very narrow set of human needs because the potential of the raw materials is vast whilst the uses that we put them to are relatively narrow. Further, there are a vast range of problems (needs) that we are yet to find solutions for. Some of these problems (needs) are for more pressing than the needs that we have fulfilled through technology. As such, the relationship between needs and technology is far from direct, and unequivocally unilinear.
Obviously we need to build into our argument the complicating external factors of access to technological materials, access to re-combination knowledge and technologies, powers of access to realisation and implementation, culture etc. All of these factors could, arguably, hinder the pursuit of knowledge.
The further, and bigger, knowledge question is the cause of the identification of needs. Are those things that we have identified as needs actually the needs which need fulfilling ? Do those needs necessarily help our pursuit of knowledge ? or is it possible that that which we identify as a need to augment our pursuit of knowledge ends up hindering our pursuit of knowledge ? The logical consequence of this argument is that it has to accept totalitarianism (which results in repressing alternative viewpoints, burning books, censoring the internet etc) is a political technology which fulfills a need which helps our pursuit of knowledge. Obviously I have picked an extreme example, but it is the outliers / exceptions that can illustrate the problems in the original argument (Popper’s Black Swans).
Technology changes our perspective on the world.
If we take the conventional tech as tool argument then we can argue that technology helps us to better understand the world around us. In its role as ‘revealing’ the world it clearly helps us in our pursuit of knowledge. This argument is fairly easy to make with many pre-industrial technologies, particularly those that generally worked in balance with the natural world (eg windmills, waterwheels, hunter-gatherer societies etc). However, it could be argued that technology which enables intensive exploitation of the natural world changes our perspective, and therefore our knowledge, of the world.
The argument here is that modern technologies enable such a high degree of manipulation and exploitation of the natural world that we stop seeing the natural world as a place that we fit into, but a place that has to fit to our needs. As such modern technology elevates us to a position of supreme mastery over the natural world. In turn this means that rather than pursuing knowledge of the world per se modern technology enables us to pursue knowledge of the world for purely extractive and exploitative purposes. As such this hinders our pursuit of knowledge as it limits the perspectives and potentiality that we see in the world.
Closing Thoughts.
As with all good ToK the key terms in the PT need to be unpacked before venturing an exploration of this question. How we define “change / help / hinder” will have a bearing on how we consider the effects of technology identified (somewhat like Aristotle’s causes !). Crucially how we explain / define ‘the pursuit of knowledge’ will influence both our definition of technology, and our judgment of the role that that which is defined has. Interestingly, the ToK Study Guide (first assessment 2020) does not define “the pursuit of knowledge”.
I would argue that the pursuit of knowledge is always contextualised, and that context will have a significant effect on the ways in which technology changes our pursuit of knowledge. This will vary by situation, place, time, knower and the knower’s purpose. In some contexts the pursuit of knowledge could be helped by technology, in other contexts it could be hindered by technology. This change could vary for the same knower, using the same technology for the same purpose in different contexts.
Finally, we come back to the original conundrum running through this series on technology - that is the causes of technology. Most students responding to this KQ may take the premise that technology fulfils needs, the needs constitute the causes of the technology, and therefore the technology helps our pursuit of knowledge. However, the fulfilment of those needs may produce a further set of needs which in turn require further fulfilment. Technology must be seen as an integral and productive part of the knowledge being pursued. The relationship between needs and technology is mutual and reciprocal, as such we could conclude that rather than helping or hindering our pursuit of knowledge technology both changes, and is in itself, our pursuit of knowledge.
Daniel, Lisbon, Jan 2023.
Other Blogs & Videos in this series:
We need to talk about Pune, India.
What do we mean by Technology?
References and Bibliography.
Bell, Kenton. “alienation definition.” Open Education Sociology Dictionary, https://sociologydictionary.org/alienation/. Accessed 24 January 2023.
Bimber, B. “The Internet and Political Transformation: Populism.” Polity, no. 31, 1998, pp. 133-160. https://www.jstor.org/stable/25655419.
Carr, Nicholas G. The Shallows: What the Internet Is Doing to Our Brains. W.W. Norton & Company, 2020.
Gillespie, Charles C. Science and Polity in France at the end of the Old Regime. Princeton, NJ. Princeton University Press, 1980.
Maxwell, Nicholas. “Science, Reason, Knowledge and Wisdom: A Critique of Specialism.” Karl Popper, Science and Enlightenment, UCL Press, 2017, pp. 233–90. JSTOR, https://doi.org/10.2307/j.ctt1vxm8p6.14. Accessed 23 Jan. 2023.
Mokyr, Joel. “The Intellectual Origins of Modern Economic Growth.” The Journal of Economic History, vol. 65, no. 2, 2005, pp. 285–351. JSTOR, http://www.jstor.org/stable/3875064. Accessed 20 Jan. 2023.
Netessine, Serguei. “Why Tom Cruise Is Still Bankable: Debunking the Long Tail.” Knowledge at Wharton, 15 December 2017, https://knowledge.wharton.upenn.edu/article/tom-cruise-threatened/. Accessed 24 January 2023.
Peterson, Richard A. The Sociological Quarterly, vol. 6, no. 1, 1965, pp. 83–85. JSTOR, http://www.jstor.org/stable/4105309. Accessed 24 Jan. 2023.
Prince, Derek J. de Solla. “Notes towards a Philosophy of the Science / Technology Interaction.” In The Nature of Knowledge: Are Models of Scientific Change Relevant ? edited by Rachel Laudon. Dordrecht: Kluwer. 1984
Rosenberg, Nathan. “Adam Smith on the Division of Labour: Two views or One ?” Economica 32, no. 126 (1965); 127-39
Valentino, Nicholas A., et al. “Selective Exposure in the Internet Age: The Interaction between Anxiety and Information Utility.” Political Psychology, vol. 30, no. 4, 2009, pp. 591–613. JSTOR, http://www.jstor.org/stable/25655419. Accessed 24 Jan. 2023.
Woodward, James. “A Functional Account of Causation; or, A Defense of the Legitimacy of Causal Thinking by Reference to the Only Standard That Matters—Usefulness (as Opposed to Metaphysics or Agreement with Intuitive Judgment).” Philosophy of Science, vol. 81, no. 5, 2014, pp. 691–713. JSTOR, https://doi.org/10.1086/678313. Accessed 11 Jan. 2023.
What is Technology?
Many of the explorations of knowledge questions in the Knowledge & Technology unit of ToK start with the question: "well, what do we mean by technology ?". So, I thought it would be useful to put together a blog which summarises 4 main approaches to how we can think of technology in its relationship with knowledge.
These approaches are very much umbrella approaches - they are rough ideal types to help us to explore that relationship between tech and knowledge, remember the focus is on knowledge, not tech.
The "tech is tool" approach.
The argument here is quite simply that technology is a tool that we use to solve human problems. This is obvious when we look at modern technologies such as the internet, cars, the printing press etc. It then also becomes apparent when we consider technologies from pre-industrial era such as smelting metals, wattle and daub etc.
This approach quickly takes us into non-physical technologies such as mathematics is a technology which allowed us to solve the problem of navigation through map-making, art is a technology which allows us to solve problems of expression and social cohesion etc. Arguably, language is the ultimate technology which allows for all other technological (& therefore knowledge) innovation. This approach has been well explained in the books by Yuval Noah Harari (particularly Sapiens: A brief history of Humankind).
Among the many writers who have taken the "tech is tool" approach are Plato and Rousseau who both argued that technology had a rather negative effect on knowledge and humanity. In Phaedrus Plato argued that that the use of writing had a negative impact on people's ability to remember and think critically. Jean-Jacques Rousseau, wrote about the dangers of technological progress in his work "Discourse on Inequality." He argued that the development of technology had led to the development of private property, which had in turn led to social inequality.
On the other hand, Francis Bacon and Karl Marx, are writers who, in taking the "tech is tool" approach see technology as a positive benefit to the pursuit of knowledge, and the development of humanity. Bacon saw science and technology as being a single unified entity. He argued that science was the best way to uncover universal ordered truths from the disordered chaos of nature. Marx saw technology as a means by which proletarian labour (& bourgeois extraction of it) is quantified, and therefore is a necessary stage for the realisation of socialism. As such, Marx was positive about the influence of technology on the pursuit of knowledge.
I think that this approach is implied, and assumed, in the knowledge questions included in the ToK Study Guide for Knowledge and Technology. This approach may be all that is required of the ToK learner. However,
However, there are some concerns with this approach, concerns which are both general for us as learners, and specific to ToK:
Did these problems, which technology apparently solves, come before the technology or did technology create these problems ? (the problem here is one of causation - what is the cause of an object ?)
If the problems are antecedent to the technology, and technology is the solution to them, then are technology and knowledge actually separate entities ?
If technology and knowledge are intertwined then is there any non-technological knowledge ?
Wider ontological problems arising from the above - if knowledge is a requisite for existence, then is technology also a requisite for our existence ? Are we defined by solving problems ? Is consciousness essentially a task focussed process (Heidegger).
Concerns #1 & #2 conveniently segue into our second approach.
The "Some knowledge is tech" approach.
This approach argues that the knowledge which gives rise to the technology developed to solve the problems that we face is in itself technology. Knowledge such as language (incl. digital coding languages), religion, scientific theories, artistic arrangement etc all give rise to specific technologies which help us to solve a set of problems.
In this approach we start to understand technology as a set of practices rather than merely as a set of objects. Both the object (artifact) and the practices (processes) are seen as being technology. The object itself might be termed "instrumentality" as it was produced to (instrumentally) change the environment - ie to solve a problem. The practices which brought the artifact into being might be termed "productivity" as they gave us an object which, at some point, gave us increased control of our environment for a required purpose. The effect of this categorisation on the acquisition and production of knowledge will be explored in greater detail in subsequent blogs.
This approach also opens the door to a consideration of the social environment within which needs arise, and knowledge develops in order to meet those needs. Of course, this brings a sharp focus on what we define as 'needs', and who has the attendant power to solve that which they define as 'needs' (a quick sub-question: a lot of technology serves 'improvement' - is improvement fulfilment of a 'need' ?). And again, we have significant problems of causation here - what is the order of causation ? Is causation a necessary, or merely, sufficient requirement for the acquisition and production of knowledge ? etc
Overall, this approach also poses a number of challenges for our theory of knowledge:
Is the technology causal to the knowledge or vice versa ? (think about examples - this is more problematic than it first appears).
Both knowledge and technology can be thought of as evolutionary (and sometimes revolutionary) - does knowledge cause technology to evolve, or vice versa ? , and if so, how ?
Do we produce some knowledge which is not to solve problems ? , and if so what, and why ?
A range of ontological questions arising from #3: are we solely a problem solving being ? what about non-problem solving behaviours ? (do they even exist in this definition?). Is consciousness contingent on
Challenge #3 conveniently segues into our next approach.
The "all knowledge is tech approach".
If we accept that technology is a tool to solve problems, and that we accept that that which is known about the world is acquired, pursued and produced to solve problems then we arrive at the position that all knowledge is technology. Conversely, all technology is knowledge (however, this is a little more obvious, and a little less overwhelming). This approach gives rise to some very significant challenges:
Is there any knowledge which is not technology ? We can unpack this question by positioning problems as time, person and situation specific. ie we know things but may not be using them to solve a problem at that moment in time. Someone, somewhere else, may have used that knowledge to solve a problem, and once created this knowledge has been passed to me. This gives rise to a second problem:
Why do we have knowledge which does not solve problems for us ? If we accept that all knowledge can be categorised as technology, and that technology solves problems for us, then why do we know things which don't solve problems for us ?
Our now familiar ontological questions are now even stronger - if all knowledge is technology, and knowledge is a necessary requirement for our existence then this approach inevitably leads to the position that to being human is being technology, or put another way that a human being is technology itself.
And so we, conveniently, segue into our final approach.,
The "we are the tech - unified being approach".
OK, so now we need to work a little out of the realms of conventional ToK, but only to give us better ways to explore some of the ToK KQs posed in the optional theme Knowledge and Technology. Some writers have argued that our very existence, - our very human 'being', is one and the same as technology. Put simply we are technology. This approach, as the culmination of the 3 earlier arguments, aggregates those arguments to posit our 'being'ness as constituting a problem solving set of processes. This is often characterised as consciousness - the idea that consciousness is a referenced intention in the world.
This approach really helps us to start to answer questions about the role of technology in changing our pursuit of knowledge. Rather than tech merely improving, or impeding, our pursuit of knowledge technology reveals the world, and therefore is our very consciousness, our very awareness of the world - it neither improves nor impedes, but in its role as revelation is consciousness itself. This will really help us when we get to questions concerning artificial intelligence, and the biological integration of technology.
However, like the other 3 approaches, this approach poses some significant challenges for our understanding of the role of technology in the pursuit of knowledge:
1. Ethical issues - If tech & being human are one & the same thing, but there is unequal access to tech then is there also unequal access to the experience of being human ?
2. Continuum issues- where does the individuality of the knower begin, and the external universality of tech end ?
3. Categorisation & Organisation issues - why do we bother to have a separate category of knowledge called 'technology' at all ?
Hold Up!! - some of that has nothing to do with ToK!
"Some of those points above appear to be way beyond the scope of ToK". When we start to consider ontological questions such as the nature of existence, the requisite conditions for existence, and the nature of consciousness it appears that we are going well beyond the requirements of the ToK course. However, I believe that we can only tackle some of the technology KQs by considering some of the questions which might (conventionally) be asked by people who we label as existentialists, phenomenologists and ontologists. This will become far clearer when we get to the post on Artificial Intelligence.
Hold Up!! - again!
"Your 4 approaches are all based on one premise. They're all based on, and developments of Approach #1 - that technology is a tool to solve a problem".
Yes - this is a legitimate challenge to the framework outlined here. An equally valid approach would be to start from an entirely different premise, maybe that technology is not caused by problem solving, that technology is caused by, and defined by something entirely different. However, that's a big undertaking - maybe one that I will need to explore in another blog.
Closing thoughts.
We can use these definitions to help us to start to explore some of the knowledge questions in ToK Optional Theme Knowledge & Technology. We will look at 3 broad areas:
How does technology change our pursuit of knowledge?
Is Artificial Intelligence changing our understanding of knowledge?
Ethics and technology.
Those blogs are coming up in the next few weeks - I hope you come back to read them then!
Daniel, Lisbon, Jan 2023
Of further interest on Knowledge & Technology is:
We need to talk about Pune, India.
Women in STEM lesson (for teaching perspectives)
Did Photography change painting?
Bibliography and References.
Bimber, Bruce, 1990, “Karl Marx and the Three Faces of Technological Determinism”, Social Studies of Science, 20(2): 333–351. doi:10.1177/030631290020002006
Franssen, Maarten, and Gert-Jan Lokhorst. “Philosophy of Technology (Stanford Encyclopedia of Philosophy).” Stanford.edu, 2009, plato.stanford.edu/entries/technology/.
Plato. Plato's Phaedrus. Cambridge :University Press, 1952
Rousseau, Jean-Jacques. Discourse on Inequality. 1755. Aziloth Books, 2013.
Weeks, Sophie. 2008. “The Role of Mechanics in Francis Bacon’s Great Instauration”, in Zittel, C., Engel, G., Nanni, R. & Karafyllis, N.C. (eds.), Philosophies of Technology: Francis Bacon and his Contemporaries. Brill. pp. 133-195.
Yuval Noah Harari. Sapiens: A Brief History of Humankind. 2011. Random House Uk, 2019. https://www.bbc.com/future/article/20190207-technology-in-deep-time-how-it-evolves-alongside-us
Value Judgments in AoK History
Is it unfair to judge people & actions of the past by the standards of today?
I put together this lesson both because making value judgments in AoK History is a 'hot topic' in the current political zeitgeist sweeping academia and campuses, but also (and mainly) because some of my ToK students have been discussing it in their History class. I wanted to show how we could approach answering the question using Knowledge Arguments (or "perspectives").
This is the second lesson in the unit on AoK History. The first lesson can be found here.
This lesson focuses on two key ToK Skills:
Using knowledge arguments to explore a knowledge claim.
Identify the possible implications of a knowledge argument in terms of a knowledge claim.
ToK Skills Diagram Click here.
Lesson Structure.
Upfront teaching (Using the Presentation linked).
I've gone for some 'upfront' teaching at this point for 2 reasons:
My DP1 ToK class have requested more upfront direct instruction - I guess everyone has their fill of constructionism at some point !
I need to start bringing together some of their (burgeoning) ToK knowledge to show how it can be structured into an answer.
The presentation starts with a Mentimeter Poll - I find this is a great way to get engagement, and to give some RLS to form the basis of the ToK discussions.
Student active learning on historical judgments
Student Group Work.
Students form groups around the perspectives on the 'fairness' of historical judgements that they agree with.
Each group is to answer 3 questions:
Why did you choose this perspective rather than the other 2 perspectives ?
Explain one implication for Historical Knowledge of your perspective (using historical RLS).
Explain one implication for historical knowledge of one of the other perspectives (using historical RLS).
The challenge of the group work will probably be to keep the students focussed on the knowledge issues rather than the historical examples. The wording of the KQ from the ToK Study guide uses the word "unfair" which could be easily interpreted as a call to emotion. However practising the skill of extrapolating knowledge points from RLS will be of great benefit when the students do the ToK Exhibition and Essay.
Implications of knowledge arguments.
The group work also requires the students to consider the implications of knowledge arguments. This is a skill which I have found student's are particularly challenged by. Again, we're trying to build this for later assessments, as it is key to accessing the higher mark bands in the ToK Essay.
Closing Thoughts.
This lesson requires the students to exercise their "Historical Imagination" (to a degree), and then to transpose that into the ToK environment. We try to find a shortcut to that process by providing them with the ToK arguments at the beginning of the lesson. This is one of the ways of trying to build higher level skills in a restricted period of time. Obviously, this is only done because the students will have spent sustained time in more constructivist learning engagements earlier in the course.
There's more on ways to develop ToK understanding in the limited time available in the post linked here.
If you have any suggestions for teaching AoK History please do not hesitate to get in touch Daniel@TokToday.com. Wishing you a great day,
Daniel, Lisbon, Jan 2023
Bibliography
Little, Vivienne. “What Is Historical Imagination?” Teaching History, no. 36, 1983, pp. 27–32. JSTOR, http://www.jstor.org/stable/43254801. Accessed 16 Jan. 2023.
Historical "Truth" - AoK History
Today's lesson explores the issues surrounding the reliability and validity of knowledge in AoK History. The lesson allows students to draw upon a wide range of ToK Concepts, and is purposefully open ended. The lesson allows students to explore the idea of a "historical Truth" (and hopefully to problematise such a term). As such it should be equally accessible for students who don't study History as it is for those who do. A
Students can draw upon the range of ToK concepts, maybe with particular reference to Objectivity, Perspective, Interpretation, Evidence and..., TRUTH
The lesson.
President George W. Bush at Emma E. Booker Elementary School in Sarasota County, Florida, September 11th 2001 as he was informed of the 9-11 attacks on The World Trade Centre.
What's wrong with this photo?
A few interesting sources:
Student collaborative thinking:
Working in Groups brainstorm some answers to the following questions. Add your answers to the Presentation for feedback to the whole group:
Can you establish the Historical Truth of this photo ? (All groups)
If a range of sources disagree how do we establish objective facts ? (Grp 1)
What counts as a fact in history? (grp 2)
Is a history which is internally consistent necessarily true ? (grp 2)
Could more than one version of the past, even contradictory ones, be internally consistent ? (Grp 3)
Is a fact always the truth ? (& vice versa: is the truth always factually accurate ?) (Grp 4)
Is it important to establish historical objectivity, and if so why ? (Grp 5)
Is it unfair to judge people and actions in the past by the standards of today? (grp 6)
Should terms such as “atrocity” or “hero” be used when writing about history, or should value judgments be avoided? (Grp 7)
Other TokToday content that ToK Teachers may find useful:
A whole unit of Natural Science Lessons (Free).
AoK Human Science Lesson on Cargo Cults of Melanesia (also use for Indigenous Knowledge) Free.
"Why do the best ToK essays get mediocre grades ?"
Today’s question was requested by a channel viewer, I hope that this situation has never happened to you, it’s certainly happened to me and my channel viewer a few times over the years.
The situation: Your ToK cohort has written essays across the range, at the top of the range are a handful of very able students who have been highly engaged in ToK. They have been enthusiastic, near absorbed, in the essay writing process - having frequent consultations with you, doing extra reading, extending their ToK knowledge beyond the class. They have written sophisticated essays that you have mentored them through. You send the essays off for assessment, and when you get the results these few students are graded at 4-5/10, whilst other competent, but far less stunning, essays have been graded 8-10. Why does this happen ?
I’ll go through some of the reasons why this might occur, and more importantly the steps that we can take to minimise it happening in the future.
Cause 1: Familiarity - missing out the basics.
Undoubtedly you, as the teacher, have been on the ToK journey of essay development with these students. You may have shared in their excitement at exploring new ways to answer the question, you will probably have participated in the development of their knowledge arguments, evaluations & implications with them. You have been a co-constructor, strictly adhering to academic integrity, with them. This leads to a high degree of familiarity with the final essay, its development and its meaning.
Unfortunately this high degree of familiarity can lead to a potential degree of ‘holism’ on our part - we might see things in the essay which are not evident to those who have not been on that journey with us. For example we might see that definitions, clarifications, and limitations are inherent to that beautifully written knowledge argument because we were with the student at every iterative stage of the integration of those elements into the argument. However, the examiner who has not been on that 6 month journey with the student may not see that background. Now, I’m not saying the examiner is wrong - they mark what is in front of them, I’m saying that the problem lies with our human ability of interpretation - we can’t help but bring all of our schematic knowledge to an essay when we read it - as such it reads very differently to you as the teacher than it may read to a removed examiner. Unfortunately it’s an essay rather than a Viva Voce.
Covering the basics - the Examiners are asked to use Global Impression Marking - a holistic approach, but they are also asked to use an assessment rubric. One of the first things the examiners might do is to check that the basic elements of the assessment rubric are in place in order to place the essay in one of the marking bands. Those basic elements may no longer be sufficiently evident in an essay which has become highly developed over successive iterations. Those elements are evident to the teacher with the background knowledge, but may be too implicit for the examiner. Further, the examiner may have to make assumptions about the student’s understanding of those basic elements in order to credit them. An essay will not fair well if the examiner has had to attribute a number of assumptions to the student when using Global Impression Marking.
2. Tacit Assumptions
A second, possible, cause of the disappointing grade are the tacit assumptions of the teacher regarding the knowledge claims and evaluation points. As the highly engaged student iteratively develops their essay they may accept assumptions in one version of the essay in order to develop their argument in the next version. In the dialogue between the teacher & student it’s possible that the tacit acceptance of these assumptions are lost - obviously to the examiner they’ll just be ‘absent’.
The challenge for the student is that they only have 1600 words to do something that’s very difficult. For the most able students it is very tempting to conflate arguments, concepts and evaluation points in order to meet the word limit. In doing so they risk somewhat ‘overegging’ the pudding - ie writing something that’s far more complicated than it needs to be, and possibly doesn’t evidence the basic requirements of the essay.
3. Seeds and Tolerance.
The final possible cause of the mediocre grades are to do with the mechanics of the examining procedure, specifically with the Seeding and tolerance processes. In order to ensure the reliability of the marking examiners are given essays which have already been marked by The Chief Examiner (these are called seeds), the seed appears to the examiner just like any other essay, and they have to mark it within 1 mark of the Chief Examiner’s mark to continue marking. If they mark more than 1 mark more / less than the CE mark they are deemed to be out of tolerance, and are suspended or withdrawn from marking for the remainder of the session.
When I was an examiner I was constantly ‘seed wary’ - I think this is the purpose of the system, and it’s a good thing. However, it does make you extra cautious when you see an atypical, or unusual, essay - all of the “Is this a seed ?” alarms go off at full volume. As such you can become extra cautious, only attributing that which is absolutely evident, and solidly justifiable. Now I know that this may not be the case for other examiners, they may not be ‘seed wary’, they may be confident in their assessment of atypical essays - I’m just being honest about my experience - and with most things in life - if it’s like that for you it’s probably like that for others too.
So, it seems like I paint a fairly depressing picture - if you stopped reading now you could go away with the message that you shouldn’t let your most capable ToK students extend themselves. But that’s not my message at all. There are ways that those students can write extraordinary essays so long as we build in a few safety mechanisms - let’s move onto the solutions:
Solutions:
1. Signpost the basics.
The first solution is to have your students Signpost the basic elements (Definitions, Knowledge Arguments, evaluation, real world examples, implications). As the essay is developed they might remove the signpost labels (eg “My knowledge argument is”), but the signposted content needs to remain. They could highlight these basic elements in early iterations of their draft in order to ensure that they keep them in place in subsequent iterations. Before finally hand in you could ask them to recolour those basic elements to ensure that they are still in place.
Further, peer review of identification of those elements would also be very helpful both in the early and latter stages of the essay process.
2. Depth rather than breadth.
The second solution applies to all students, but may be particularly pertinent for those students who are finding it difficult to fit all of their arguments into the 1600 word limit. Some of the Prescribed Titles contain multiple clauses, a number of assumptions, and various approaches inherent to answering the PT. It is generally better to develop a limited number of arguments in depth rather than to try to answer all possible aspects of the PT with far less depth - i.e. depth rather than breadth. It may be necessary to explicitly state which aspects of the PT will be challenged and why (ie signposting).
This is also of particular relevance if the student is tempted to conflate multiple aspects of the PT in order to cover a wider range of arguments - a general rule of thumb would be to focus on developing a substantial argument to a more conventional interpretation of the PT rather than conflation which runs the risk of an inadvertent rewriting of the PT - which will definitely lead to a mediocre grade, or potentially worse.
3. Blind Assessment & Blind Moderation.
This is possibly the most effective preventative measure that we can take as teachers. If we can undertake blind assessment procedures at the Draft stage, and possibly at final assessment stage we counterbalance some of those familiarity problems. At the most basic level just ask your students to submit their Drafts without their names on them, but obviously far more effective is to swap your class’s essays with other teachers at your school. If you’re the sole teacher at your school, or you have a small cohort then swap essays with others in your ToK network. We started to do this at my previous school a few years ago and the experience was revelatory for our team. We picked up on many problems in the essays from each other’s classes that we hadn’t seen until that point, it was also super useful to get colleagues' ideas on the approaches and content of the essay - this definitely had a significant positive effect on our essay scores.
So, that’s my experience, and my suggestions for solutions. If you have differing experiences, or other solutions I’d love to hear them in the comments section. Today's blog was suggested by a TokToday subscriber, if you have questions or content that you would like me to cover please don't hesitate to get in touch (Daniel@TokToday.com),
Have a great day,
Daniel, Lisbon, Jan 2023
5 (more) essential tips for marking the ToK Essay Draft.
This post comes in response to feedback from a video that I made on marking the ToK Draft Essay (we’re all starting in different places on this ToK journey) - those a little further on their ToK Journey as I focus more on the actual assessment of the content of the essay.
Now, I can’t replicate an IB Cat 2 workshop, neither would you want me to, so what I will do is run through 5 big checks that I make of the content of the essay when I’m marking the Draft.
1. Is the focus of the essay on knowledge arguments or on RLS ?
Most Draft Essays that I have seen have far too much RLS content, they’re overly descriptive of the examples used, and dedicate far too many words to the RLS. This is entirely understandable - DP students have probably spent most of their educational career being assessed on the accuracy of their understanding of content. In ToK we’re asking them to do something a little different - we’re asking them to look at the construction of that content - this is a leap that many students find difficult to make. We can use the rough 80-20 rule here: 80% knowledge to 20% RLS description.
I usually ask students to highlight knowledge & RLS content in different colours on their Draft essays to help them to have a visual representation of the difference.
2. Are definitions consistent throughout the essay ?
Most students realise that the definition of concepts and terms is key to being able to write the ToK Essay. However, students will often change their definitions, or even forget about them during the writing of the essay. Changing definitions can be a strong evaluation point if they find that an earlier definition was insufficient, but this must be done explicitly, and shown to be grounded in the exploration of the PT.
Recent examiner’s reports have advised that students define terms within each AoK rather than at the beginning of the Essay - this is a good way of reducing the risk of inconsistency in definitions within the essay.
3. Rough Band Placement. (pyramid)
The essay assessment instrument has 5 ‘bands’ or ‘levels’, IB have provided characteristics for each band. I think we can consider broadly what we will find in each essay band - I call this the assessment pyramid.
General talk through the pyramid, as shown in Canva Slides.
Now, I know that this is just a further precis of the assessment rubric itself, but sometimes there’s a wood & trees problem - this is my attempt to see the wood.
4. Signposting & LTQ.
Sometimes the Draft Essays can be super confusing: concepts, AoKs, Knowledge Arguments, RLS are all mixed together, sometimes contradictory, often incoherent. If they’re confusing for us as teachers imagine what they’re like for the Examiners. A simple way to start to unravel is to ask the student to signpost the key elements of the essay. I ask them to add sentences which show me where the main elements are, for example:
“My Knowledge claim in AoK ____ is…,”
“This is supported by the RLS ________”
“An evaluation of this argument is _________”
And most importantly by adding a sentence at the end of each paragraph that starts with:
“Linking back to the prescribed title this means that……,”
Signposting the main elements, and explicitly linking back to the question means that there is very little risk of the essay being assessed in the lower 2 bands. It ensures that the student has the basic elements in place to get at least 4/10, which means they’ll pass ToK.
Signposting also helps the examiners who are serially reading essays written in a range of styles, of varying quality etc. The examiners are looking to award marks, this is much easier to do if students have signposted the content in their essay.
If the signposting is a little clunky after they have developed their essay it can always be taken out before final submission, just like stabilisers on a kid’s bike.
Team work makes the dream work.
5. Team Work:
My final tip is teamwork. Assessing ToK Essays is no easy task, it’s complicated for everyone - I’ve seen senior examiners significantly disagreeing over the marks awarded to an essay. So, it’s important to remember that no ToK Teacher is in this on their own. Work with other ToK Teachers either in your school, or in your local ToK network. If you are the sole ToK teacher in your school, and there is no local ToK Network get in touch with another DP school- I have always found colleagues to be generous and welcoming.
In collaboration with other ToK teachers you can problem solve, moderate, and standardise. You can pick apart exemplars and share previously assessed essays. No one expects you to have all the answers, this is a synergistic process, and we’re all in this together.
I hope that you found those tips useful. If you have any suggestions for further content please don't hesitate to contact me, Daniel@TokToday.com.
Have a great day!
Daniel, Lisbon, Jan 2023
Applying The Scientific Method
Over the years I have found that if students have a good understanding of The Scientific Method in ToK it helps them in many ways across all Areas of Knowledge, and in both The Exhibition and The ToK Essay. The Scientific Method gives students an understanding of the tensions between reliability and validity of knowledge, and therefore more useful ways to discuss accuracy, reality, and even 'truth'.
Most crucially teaching the scientific method gives students key foundational knowledge for both AoK Natural Sciences and AoK Human Sciences (which saves precious teaching time). I recently started AoK The Natural Sciences with my DP1 ToK class, and therefore started by teaching The Scientific Method.
The Scientific Method Lesson.
I started with a simple card sort: Sort the cards into the correct order of the Scientific Method. Many students have already studied the scientific method, so this is just revision of previous knowledge.
The Card Sort Resource is included at the end of this blog.
Chalk & Talk !
I know that it's very naughty in our new constructionist age, but I do think that there's still a place for a bit of chalk n talk every now & then. So, I followed up the card sort activity with a quick run through of the Presentation linked here. We can look at this as an introduction to the main task...,
The Task
The task is designed to actively engage the students in applying the Scientific Method. I love using drama in the ToK classroom - I think that drama really brings the subject alive for students who either find ToK challenging, or tbh just don't see the point of it (if you have never had either of these groups of students you are fortunate). By using drama the students don't have to risk "getting things wrong" - it is merely a role that is "getting it wrong".
The task itself is fairly self explanatory, I ask the class to play the role of "Funding Committee" when they hear the presentations, this moves them from passive observers to active engagers.
The task sheet is included as a PDF at the end of this blog.
End thoughts
My approach to teaching ToK is directed more towards the students who find the subject difficult, or have little interest in the subject. This doesn't mean that the interested & engaged can't get a lot out of the lesson - I simply think that "a rising tide lifts all boats". As such I try to design lessons which maximise engagement, games and fun (despite this one having some chalk n talk in it). I really don't think that reading long articles, or watching TED videos is either a necessary nor optimal way to learn ToK. - I have used my "Drama + Games" approach for many years, and my students have always achieved excellent results...,
I hope that you found today's resources useful. If you have suggestions or requests for further topics / resources please don't hesitate to let me know.
Wishing you a great weekend,
Daniel,
Lisbon, Jan 2023
The Sorting Activity Resource is linked below, just print & cut out.
Scientific Method Sorting Activity
The Task Sheet.
AoK Natural Sciences: A whole Unit of ToK (Free)!
I'm currently writing a new set of lessons for teaching AoK Natural Sciences, and I came across this set of lessons from my old teaching website that I used when I was teaching in Bangkok. There are a lot of resources linked into these lessons, and it's beautifully presented - so I thought I'd offer it out there to the TokToday community (for free).
The focus of the unit is on the organisation of knowledge in AoK Natural Sciences
I think I was having fun with the presentation when I put this together!
My teaching pace and focus with my current ToK students is a bit different to the classes that I put this module together for, so I prefer to rewrite the unit than just use this old unit. However, there's a lot in here - so it may be of use to ToK Teachers reading this blog. If you're pushed for time, and need something ASAP it may be something that you can just pick up and use.
I will publish my new lessons on AoK Natural Sciences in the next week or so, I am focussing on a more active and accessible set of activities.
Other resources which may be of use are:
Jahn & scientific anomalies (a useful RLS).
A presentation about Natural sciences.
If you found these lessons useful, or have suggestions for upcoming content that you would find useful, I would love to read your comments below.
Have a great day!
Daniel,
Lisbon, Jan 2023
ToK Essay Titles as Philosophy..,
I know that ToK is not Philosophy, and I know that it's bad form to use the term 'philosophy' when talking about ToK, and I know that you don't have to be a philosophy teacher / student to teach / learn ToK.
BUT
there do seem to be some interesting philosophy parallels, and I'm interested in philosophy - so here goes...,
Q1: Is replicability necessary?
I'm certainly seeing Black Swans and White Swans (thank you Mr Popper), but I'm also asking - maybe this is the classical logic of truth functions in philosophy. (remember those afternoons in Logic 101 pondering that if all men wear a blue hat, Sam wears a blue hat, is Sam a man?)
or is it an ends vs means question (without the ethical - moral implications)?
Q2 - Is the explained or unexplained more important?
Is this Schrodinger's Cat?
or is this Rumsfeld's Known Knowns, Known Unknowns and unknown unknowns?
Is it Realism vs Anti-realism?
Or is it Empiricism vs Rationalism, are all of the questions empiricism vs rationalism?
Q3 - Do bubbles matter ?
I'm imagining a tree falling in an empty forest without anyone there to see, or maybe hear it.
Or am I hearing aSocratic dialogue on the relationship between ignorance and evil, and the involuntary nature of evil acts ?
Or is this Plato's and Pareto's Elite Theory ?
Q4 - Are we astonished that so little knowledge can give us so much power?
I'm seeing angels dancing on a pinhead, but that's not actually philosophy, nor ToK.
I'm seeing more map metaphors and low hanging fruit metaphors.
Is this the Process Philosophy vs Substance Metaphysics? Sort of Dewey & James vs Quine & Schaffer?
Q5 - Are visual representations helpful in the communication of knowledge.
Do I see the metaphor of the map sailing back over the horizon..., oh long lost metaphor how we have missed you! Where did you go?
Or maybe it's about Structuralism, Semiotics, Sign and Symbol - Levi-Strauss come forth and elucidate, illuminate and educate!
Q6 Does methodology determines knowledge produced?
This is empiricism - rationalism, it's got to be that old chestnut- is knowledge discovered, or is it constructed ? Judging by views on my videos this is the least popular ToK Question this year, yet in my opinion it's the easiest question posed. Maybe our ToK students love a challenge !
Maybe it's Foundationalism vs Coherentism, I'm sort of drawn to the idea that it's about the degree and form of justification required to consider something as knowledge.
End thoughts
I apologise if I have missed some glaringly obvious philosophical question, my excuse is that my degree is in Economics, not Philosophy. If I have missed some obvious philosophy I would love for you to add your thoughts and insight in the comments section.
I initially posed this post as a bit of fun, however as a ToK teacher I have always found it useful to look at some of the philosophy behind the questions. It helps me to give more advice to students on the sorts of sub-questions that could extend their thinking. It also helps me to guide them in developing counterclaims.
If you want to watch my videos on the M23 titles they can be found at this link.
If you want a guide on how to write the ToK Essay - check out our e-book (linked).
If you want to know about the ToK of the individual Essays check out the blog posts (there's one for each essay)
If people are interested I can put together a post going into more detail on the philosophical questions mentioned in this post, just let me know.
Enjoy your ToK writing and thinking,
Daniel,
Lisbon, Dec 22
ToK Essay 5 May 2023: What is meant by "helpful in the communication of knowledge?"
Since writing the essay notes, and making the video, for ToK Essay 5 May 2023 (Visual Representations) I have been returning to the phrase “helpful in the communication of knowledge”. I feel that more unpacking of this term will be useful for students writing this essay.
For the wider (more introductory) notes on ToK Essay 5 May 2023 see this video, and pick up the detailed notes from here.
In this blog post I look at 3 perspectives that students could use to approach / unpack this term:
1. Ways of understanding the typology of knowledge (Zuckerfeld, 2017)
2.The role of symbol and signal in the communication of knowledge.(Berry, 2019)
3. The role of meaning in the communication of knowledge.(Hornsby & Stanley 2005).
My starting premise for all of these perspectives is that in order to judge whether something is helpful we have to decide what its purpose is, in this case we have to decide what the purpose of the communication of knowledge is in order to judge whether visual representations are helpful. This post focuses on the question - what are some of the possible purposes of the communication of knowledge ?
Perspective 1: The Typology of Knowledge (Zuckerfeld 2017).
Students could explore whether the communication of knowledge is helpful for the individual knower or for the development of the Area of Knowledge as a whole. Different types of knowledge will be helpful for one, the other, and sometimes both. Obviously, this distinction will be further differentiated by the various factors of context.
When looking at the individual knower in ToK there is always the danger of falling into a relativist argument that fails to make any substantial statements. To help to avoid this I point students towards the idea of ‘intersubjective realities’ (Zuckerfeld, 2017) - the idea that knowers share common knowledge (which leads to language, culture etc).
It is in these intersubjective realities Zuckerfeld argues that we can find some answers to the purpose of communication of knowledge - at either the level of the knower or at the level of the AoK. Zuckerfeld describes 5 main types of inter-subjective knowledge to which we can apply the use of visual communication in order to assess its usefulness.
Types of Knowledge
(i) Recognition Based Knowledge
This is the institutional based knowledge (both formal and informal) (such as work & education) which enables location and hierarchy. This is value based, and as such visual representations are only useful in so much as they consolidate values based comprehension.
(ii) Linguistic Knowledge.
This includes not only formal language, but also informal language (such as slang & dialect) and non-human language (such as computer coding). Students could argue that language itself is a form of visual representation (emojis are obvious etc). The argument for ‘helpfulness’ seems clear here, however, strong counterclaims could be developed around misinterpretation and the contextual nature of meaning. The strength of formalised semantic (rather than visual) based language is standardised interpretation, as such a strong argument could be developed for the unhelpfulness of visual representations.
(iii) Organisational Knowledge.
This is knowledge which increases the specialisation and precision of process and understanding. Such knowledge allows for high degrees of human expertise, which arguably increases the effectiveness of knowledge. Zuckerfeld includes the internet, and social media such as Facebook & Youtube under this typology. Arguably Visual Representation only adds value here (ie is ‘helpful’) when it can convey greater meaning than written or spoken words.
(iv) Axiological Knowledge.
This is knowledge which defines the knower’s identity. Zuckerfeld argues that this is experienced as individual, but is increasingly consumption based. Strong arguments for the role of visual representations helping to quickly convey meaning could be developed for this type of knowledge.
(v) Normative Knowledge.
This is formalised, externalised, standardised knowledge such as laws, academic content, and rights. This is highly networked, social and public knowledge. As such a student could develop strong arguments that visual representations are helpful in the communication of aspects of this knowledge, if not so much in the production of this knowledge.
Perspective 2. The role of symbol and signal in the communication of knowledge.(Berry, 2019)
Berry et al look at the Digital Humanities as an emerging field of AoK Human Sciences, arguing that the prevalence of digital communication of knowledge requires us to redevelop the Human Sciences. Obviously, much digital communication is in the form of visual representation, and as such Berry’s article can add much depth to the Human Sciences element of ToK Essay 5 May 2023.
Berry starts with vivid analysis of the problems caused by the digital communication of knowledge at both the level of the Knower, and the development of wider (AOK) social knowledge. He borrows the term ‘disorientation’ ( the difference between the human ordering of time and the digital representation of time) from Stiegler (Stiegler 2008) to describe the effects of this vastly increased digital communication of knowledge. ToK students could develop this concept to look at the effects of visual representations in Human Sciences of the representation of the more qualitative aspects of that studied.
Berry et al propose that GAFA (Google, Amazon, Facebook, Apple) as a representation of big tech has led to a commoditization of human experience in which symbolic lived reality has replaced been replaced by the signal of the communication. As such, all knowledge becomes data which can be directly compared, calculated, and standardised. Again, ToK students would have to be careful not to spend too many words on descriptions of the negative effects of digitalisation of knowledge, but rather focus on the challenges that this poses for AoK Human Sciences which were primarily developed in a pre-digital era.
This argument then develops into the description of what they call “The Second Machine Age” in which the digitalisation of knowledge leads to high levels of anxiety and alienation. An age in which emotions are no longer represented symbolically, but become commodified signals. This argument provides a rich framework for ToK students to unpack the ideas of both visual representations (representing what ? and in which ways ?) and helpful (to whom ? and for what ?). Again, they borrow from Stiegler the idea of the “Grammatization” of culture, which could easily & equally be applied to visual representations and knowledge in the PT.
Encoding-Decoding
A core part of their argument is that in the digital age knowledge has to be encoded before it is communicated. This has two consequences: (i) the knowledge is now constrained by its compatibility to the platform of communication (ii) the decoding of the signal depends upon the receiver who is further removed from the signaller than in a pre-digital age. This argument could be developed to demonstrate the unhelpfulness in the communication of knowledge in the Human Sciences, especially in Psychology, Anthropology and Sociology. More pertinently, it is maybe an argument that those Human Sciences need to be redeveloped in order to take account of the new forms of visual representations in the communication of knowledge.
A final point of interest of their argument is a development of Drucker’s paper on digital scholarship in which she argues that “tool making has replaced hermeneutics”. Essentially she’s arguing that the production of the representation of knowledge (signal) has replaced the meaning (symbol) of that knowledge. In terms of PT#5 this could be developed as a strong argument (counterclaim ?) that the visual representation is now the knowledge itself, the visuals no longer represent the knowledge, they are the knowledge.
Perspective 3: The role of meaning in the communication of knowledge. (Hornsby & Stanley 2005).
Hornsby & Stanley (2005) take a linguistic approach to the purpose of the communication of knowledge. This is useful to us as we can think of written knowledge as being a visual representation of knowledge, and contrast it with verbal knowledge. Obviously, both are means by which knowledge is communicated.
Hornsby & Stanley make a distinction between Semantic Knowledge (knowledge conveying meaning), Practical Knowledge (knowledge enabling us to act), and Procedural Knowledge (knowledge which tells us how to do something). Their starting point is that Semantic Knowledge is Practical Knowledge, and that within practical knowledge we have the realisation of semantic knowledge. They argue that practical knowledge is developed through speaking rather than visually. This is useful for students writing ToK Essay 5 May 2023 because it provides a counterclaim to the helpfulness of visual representations . Obviously students writing this answer will have to place this theory within the Areas of Knowledge concerned (Hum Sci & Maths), however it has direct relevance within both AoKs.
Hornsby & Stanley argue that spoken language is more meaningful than written / visual language because with spoken language the meaning of the communication is integrated with the understanding of the knowledge in real time, in situ. As such the sender of the knowledge is able to adjust the message in response to the receivers comprehension in situ. They develop this argument to show that the semantic structures associated with spoken language (knowledge) are different to the semantic structures associated with written (visually represented) language (Knowledge). Again, this can be used as a counterargument against the helpfulness of visual representations, for example in the communication of knowledge in the maths classroom.
What about the communicator ?
The third string of their argument is the emphasis they place on the communicator in the production and packaging of the knowledge. The communicator shapes the semantic meaning of the knowledge in the production of the message. With visual representation of the knowledge that shaping must be done hypothetically, however with spoken communication of knowledge it can be done organically in response to the receiver.
A fourth position in their paper which can be applied to ToK Essay 5 May 2023 is the type of knowledge best described by visual representations. They argue that visual representations of knowledge best describe procedural knowledge, and that this is typical in both the production, sending and reception of the knowledge. Such procedural knowledge, they argue, is best developed as semantic knowledge in a spoken environment (e.g. think about ‘reading it out loud to make sense of it’).
Their article goes onto develop an argument concerning a ‘meta-meaning’ enshrined within spoken language (knowledge) as opposed to written / visually represented language (knowledge).
What I have presented here are 3 perspectives on ways in which we can understand the purposes of the communication of knowledge in order to judge whether visual representations are helpful. Obviously, all that I can give in the scope of this post are brief overviews of the research cited. Full references (plus doi references, or JSTOR references) are included below should you wish to read the original articles to get more details for your ToK Essay.
Should you have any questions or thoughts please do not hesitate to get in touch with me at Daniel@TokToday.com.
Enjoy your ToK Writing!
Daniel, Lisbon,
December 2022
References.
BERRY, DAVID M., et al. “No Signal without Symbol: Decoding the Digital Humanities.” Debates in the Digital Humanities 2019, edited by Matthew K. Gold and Lauren F. Klein, University of Minnesota Press, 2019, pp. 61–74. JSTOR, https://doi.org/10.5749/j.ctvg251hk.8. Accessed 30 Nov. 2022.
Drucker, Johanna. “Humanistic Theory and Digital Scholarship.” In Debates in the Digital Humanities, edited by Matthew K. Gold, 85-95. Minneapolis: University of Minnesota Press 2012.
Hornsby, Jennifer, and Jason Stanley. “Semantic Knowledge and Practical Knowledge.” Proceedings of the Aristotelian Society, Supplementary Volumes, vol. 79, 2005, pp. 107–45. JSTOR, http://www.jstor.org/stable/4106937. Accessed 30 Nov. 2022.
Stiegler, Bernard, Technics and Time: 2 Disorientation. Translated by Stephen Barker. Stanford, Calif: Stanford University Press, 2008.
Zukerfeld, Mariano, and Suzanna Wylie. “The Typology of Knowledge.” Knowledge in the Age of Digital Capitalism: An Introduction to Cognitive Materialism, vol. 2, University of Westminster Press, 2017, pp. 53–98. JSTOR, http://www.jstor.org/stable/j.ctv6zd9v0.7. Accessed 30 Nov. 2022.
AoK Natural Sciences - bits n bobs
Whilst preparing a "Christmas lesson" on AoK Natural Sciences I was digging through some of my resources and came across a long series of activities that I previously used for AoK Nat Sci.
I link them below as you may find some of them useful..., it's free & downloadable at this link.
The Christmas Lesson will be coming on Monday next week.