#pedagogy — Public Fediverse posts

🌸 The Circle of Blooms: A New Beginning

https://www.steinerskolen-norge.no/display/edecf743-196a-8056-c60f-696341957037

🌸 The Circle of Blooms: A New Beginning

https://www.steinerskolen-norge.no/display/edecf743-196a-8056-c60f-696341957037

🌸 The Circle of Blooms: A New Beginning

https://www.steinerskolen-norge.no/display/edecf743-196a-8056-c60f-696341957037

🌸 The Circle of Blooms: A New Beginning

https://www.steinerskolen-norge.no/display/edecf743-196a-8056-c60f-696341957037

🌸 The Circle of Blooms: A New Beginning

https://www.steinerskolen-norge.no/display/edecf743-196a-8056-c60f-696341957037

What are the judgements which students are making as they use AI?

I enjoyed this paper by Walton et al about the role of judgement in how students use AI. There’s currently a startling lack of rich data about the judgements students actually make in their use of chatbots, as they summarise on pg 2:

There remains little information beyond decontextualised self-reports about
what these judgements might be and how they influence what students learn (or fail to learn) from completing their assessments. Thus, educators and institutions continue to design assessments and set policy to take account of GenAI use without understanding how their choices will affect students and learning. Exploring how students make judgements with—and about—GenAI will therefore provide a much-needed perspective on how students are coming to learn with, rely on, and dissemble with GenAI.

Through a nicely designed walk through method they identify six categories of what they call judgement events: time bound occurrences where the student where a student evaluates AI and its outputs as they worked on an assessment. What I particularly like about this framing is how it enables us to distinguish between:

1. The occurance and sequencing of judgement events.
2. The (epistemically) better or worse judgement events which make up that sequence

It does what Milan and I describe in The Platform Learns To Speak as opening the blackbox of AI use in order to look at the process which underpins it. The obvious lesson to take from the notion of judgement events is to ask four questions of assessment design:

1. What is the process? Where and when are judgements called for?
2. What kind of judgement events are desirable for constructive alignment?
3. How does the logic of the design ideally knit together these judgement events?
4. How does the embedding of the assessment support or hinder this ambition?

A crucial point they make in this paper concerns student’s ability to distinguish their own epistemic contribution to the output. I’ve been prone in the last year to saying that we need to help students understand what it feels like to be learning*. I stand by this but I realise it’s a precarious achievement rather than something we can rely on. This is why I like their two points on pg 13 so much:

This makes two points: firstly, GenAI use can enhance or hinder learning,
depending on circumstance. Secondly, students’ own views of what they learnt or how they worked with GenAI use does not distinguish between these cases.

*Thanks to David Meechan for setting me off on this, when he visited us.

Hey, wait… if student self-reports are intrinsically unreliable than what does that mean for classification and declaration? This is a HUGE issue which I need to come back to.

What are the judgements which students are making as they use AI?

I enjoyed this paper by Walton et al about the role of judgement in how students use AI. There’s currently a startling lack of rich data about the judgements students actually make in their use of chatbots chatbots, as they summarise on pg 2:

There remains little information beyond decontextualised self-reports about
what these judgements might be and how they influence what students learn (or fail to learn) from completing their assessments. Thus, educators and institutions continue to design assessments and set policy to take account of GenAI use without understanding how their choices will affect students and learning. Exploring how students make judgements with—and about—GenAI will therefore provide a much-needed perspective on how students are coming to learn with, rely on, and dissemble with GenAI.

Through a nicely designed walk through method they identify six categories of what they call judgement events: time bound occurrences where the student where a student evaluates AI and its outputs as they worked on an assessment. What I particularly like about this framing is how it enables us to distinguish between:

1. The occurance and sequencing of judgement events.
2. The (epistemically) better or worse judgement events which make up that sequence

It does what Milan and I describe in The Platform Learns To Speak as opening the blackbox of AI use in order to look at the process which underpins it. The obvious lesson to take from the notion of judgement events is to ask three questions of assessment design:

1. What is the process? Where and when are judgements called for?
2. What kind of judgement events are desirable for constructive alignment?
3. How does the logic of the design ideally knit together these judgement events?
4. How does the embedding of the assessment support or hinder this ambition?

A crucial point they make in this paper concerns student’s ability to distinguish their own epistemic contribution to the output. I’ve been prone in the last year to saying that we need to help students understand what it feels like to be learning*. I stand by this but I realise it’s a precarious achievement rather than something we can rely on. This is why I like their two points on pg 13 so much:

This makes two points: firstly, GenAI use can enhance or hinder learning,
depending on circumstance. Secondly, students’ own views of what they learnt or how they worked with GenAI use does not distinguish between these cases.

*Thanks to David Meechan for setting me off on this, when he visited us.

What are the judgements which students are making as they use AI?

I enjoyed this paper by Walton et al about the role of judgement in how students use AI. There’s currently a startling lack of rich data about the judgements students actually make in their use of chatbots, as they summarise on pg 2:

There remains little information beyond decontextualised self-reports about
what these judgements might be and how they influence what students learn (or fail to learn) from completing their assessments. Thus, educators and institutions continue to design assessments and set policy to take account of GenAI use without understanding how their choices will affect students and learning. Exploring how students make judgements with—and about—GenAI will therefore provide a much-needed perspective on how students are coming to learn with, rely on, and dissemble with GenAI.

Through a nicely designed walk through method they identify six categories of what they call judgement events: time bound occurrences where the student where a student evaluates AI and its outputs as they worked on an assessment. What I particularly like about this framing is how it enables us to distinguish between:

1. The occurance and sequencing of judgement events.
2. The (epistemically) better or worse judgement events which make up that sequence

It does what Milan and I describe in The Platform Learns To Speak as opening the blackbox of AI use in order to look at the process which underpins it. The obvious lesson to take from the notion of judgement events is to ask four questions of assessment design:

1. What is the process? Where and when are judgements called for?
2. What kind of judgement events are desirable for constructive alignment?
3. How does the logic of the design ideally knit together these judgement events?
4. How does the embedding of the assessment support or hinder this ambition?

A crucial point they make in this paper concerns student’s ability to distinguish their own epistemic contribution to the output. I’ve been prone in the last year to saying that we need to help students understand what it feels like to be learning*. I stand by this but I realise it’s a precarious achievement rather than something we can rely on. This is why I like their two points on pg 13 so much:

This makes two points: firstly, GenAI use can enhance or hinder learning,
depending on circumstance. Secondly, students’ own views of what they learnt or how they worked with GenAI use does not distinguish between these cases.

*Thanks to David Meechan for setting me off on this, when he visited us.

Hey, wait… if student self-reports are intrinsically unreliable than what does that mean for classification and declaration? This is a HUGE issue which I need to come back to.

What are the judgements which students are making as they use AI?

I enjoyed this paper by Walton et al about the role of judgement in how students use AI. There’s currently a startling lack of rich data about the judgements students actually make in their use of chatbots, as they summarise on pg 2:

There remains little information beyond decontextualised self-reports about
what these judgements might be and how they influence what students learn (or fail to learn) from completing their assessments. Thus, educators and institutions continue to design assessments and set policy to take account of GenAI use without understanding how their choices will affect students and learning. Exploring how students make judgements with—and about—GenAI will therefore provide a much-needed perspective on how students are coming to learn with, rely on, and dissemble with GenAI.

Through a nicely designed walk through method they identify six categories of what they call judgement events: time bound occurrences where the student where a student evaluates AI and its outputs as they worked on an assessment. What I particularly like about this framing is how it enables us to distinguish between:

1. The occurance and sequencing of judgement events.
2. The (epistemically) better or worse judgement events which make up that sequence

It does what Milan and I describe in The Platform Learns To Speak as opening the blackbox of AI use in order to look at the process which underpins it. The obvious lesson to take from the notion of judgement events is to ask four questions of assessment design:

1. What is the process? Where and when are judgements called for?
2. What kind of judgement events are desirable for constructive alignment?
3. How does the logic of the design ideally knit together these judgement events?
4. How does the embedding of the assessment support or hinder this ambition?

A crucial point they make in this paper concerns student’s ability to distinguish their own epistemic contribution to the output. I’ve been prone in the last year to saying that we need to help students understand what it feels like to be learning*. I stand by this but I realise it’s a precarious achievement rather than something we can rely on. This is why I like their two points on pg 13 so much:

This makes two points: firstly, GenAI use can enhance or hinder learning,
depending on circumstance. Secondly, students’ own views of what they learnt or how they worked with GenAI use does not distinguish between these cases.

*Thanks to David Meechan for setting me off on this, when he visited us.

Hey, wait… if student self-reports are intrinsically unreliable than what does that mean for classification and declaration? This is a HUGE issue which I need to come back to.

What are the judgements which students are making as they use AI?

I enjoyed this paper by Walton et al about the role of judgement in how students use AI. There’s currently a startling lack of rich data about the judgements students actually make in their use of chatbots, as they summarise on pg 2:

There remains little information beyond decontextualised self-reports about
what these judgements might be and how they influence what students learn (or fail to learn) from completing their assessments. Thus, educators and institutions continue to design assessments and set policy to take account of GenAI use without understanding how their choices will affect students and learning. Exploring how students make judgements with—and about—GenAI will therefore provide a much-needed perspective on how students are coming to learn with, rely on, and dissemble with GenAI.

Through a nicely designed walk through method they identify six categories of what they call judgement events: time bound occurrences where the student where a student evaluates AI and its outputs as they worked on an assessment. What I particularly like about this framing is how it enables us to distinguish between:

1. The occurance and sequencing of judgement events.
2. The (epistemically) better or worse judgement events which make up that sequence

It does what Milan and I describe in The Platform Learns To Speak as opening the blackbox of AI use in order to look at the process which underpins it. The obvious lesson to take from the notion of judgement events is to ask four questions of assessment design:

1. What is the process? Where and when are judgements called for?
2. What kind of judgement events are desirable for constructive alignment?
3. How does the logic of the design ideally knit together these judgement events?
4. How does the embedding of the assessment support or hinder this ambition?

A crucial point they make in this paper concerns student’s ability to distinguish their own epistemic contribution to the output. I’ve been prone in the last year to saying that we need to help students understand what it feels like to be learning*. I stand by this but I realise it’s a precarious achievement rather than something we can rely on. This is why I like their two points on pg 13 so much:

This makes two points: firstly, GenAI use can enhance or hinder learning,
depending on circumstance. Secondly, students’ own views of what they learnt or how they worked with GenAI use does not distinguish between these cases.

*Thanks to David Meechan for setting me off on this, when he visited us.

Hey, wait… if student self-reports are intrinsically unreliable than what does that mean for classification and declaration? This is a HUGE issue which I need to come back to.

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Idaho Now Offering Master Gardener Online | Preston Citizen https://www.allforgardening.com/1930578/idaho-now-offering-master-gardener-online-preston-citizen/ #Bark(botany) #BehaviorModification #botany #cognition #conifer #EarthSciences #EasternMeadowVole #education #GardeningIdaho #girdling #gopher #HumanCommunication #idaho #Kingdoms(biology) #lawn #learning #NaturalEnvironment #pedagogy #Pest(organism) #PhysicalGeography #Pine #plants #Rodent #rodenticide #soil #Spruce #tree #Vole #WinterStorm

New blog post: “There are two kinds of theorems”.

Mathematics alternates between model-building and model-using. After the model building comes a series of theorems showing that the model works and makes sense. And then come the real theorems, the new ones about the thing that was being modeled.

This is perhaps the most important mathematical methodology, and it is never explained to the students, who are left wondering why Euclid proves a lot of theorems about things that are obvious (“vertical angles are equal”) or why we show that the Peano axioms can prove the commutativity of addition.

https://blog.plover.com/math/two-kinds-of-theorems.html

#math #pedagogy #mathEducation #geometry

New blog post: “There are two kinds of theorems”.

Mathematics alternates between model-building and model-using. After the model building comes a series of theorems showing that the model works and makes sense. And then come the real theorems, the new ones about the thing that was being modeled.

This is perhaps the most important mathematical methodology, and it is never explained to the students, who are left wondering why Euclid proves a lot of theorems about things that are obvious (“vertical angles are equal”) or why we show that the Peano axioms can prove the commutativity of addition.

https://blog.plover.com/math/two-kinds-of-theorems.html

#math #pedagogy #mathEducation #geometry #universeOfDiscourse

New blog post: “There are two kinds of theorems”.

Mathematics alternates between model-building and model-using. After the model building comes a series of theorems showing that the model works and makes sense. And then come the real theorems, the new ones about the thing that was being modeled.

This is perhaps the most important mathematical methodology, and it is never explained to the students, who are left wondering why Euclid proves a lot of theorems about things that are obvious (“vertical angles are equal”) or why we show that the Peano axioms can prove the commutativity of addition.

https://blog.plover.com/math/two-kinds-of-theorems.html

#math #pedagogy #mathEducation #geometry #universeOfDiscourse

New blog post: “There are two kinds of theorems”.

Mathematics alternates between model-building and model-using. After the model building comes a series of theorems showing that the model works and makes sense. And then come the real theorems, the new ones about the thing that was being modeled.

This is perhaps the most important mathematical methodology, and it is never explained to the students, who are left wondering why Euclid proves a lot of theorems about things that are obvious (“vertical angles are equal”) or why we show that the Peano axioms can prove the commutativity of addition.

https://blog.plover.com/math/two-kinds-of-theorems.html

#math #pedagogy #mathEducation #geometry #universeOfDiscourse

New blog post: “There are two kinds of theorems”.

Mathematics alternates between model-building and model-using. After the model building comes a series of theorems showing that the model works and makes sense. And then come the real theorems, the new ones about the thing that was being modeled.

This is perhaps the most important mathematical methodology, and it is never explained to the students, who are left wondering why Euclid proves a lot of theorems about things that are obvious (“vertical angles are equal”) or why we show that the Peano axioms can prove the commutativity of addition.

https://blog.plover.com/math/two-kinds-of-theorems.html

#math #pedagogy #mathEducation #geometry

'With this letter we take a principled stand against the proliferation of so-called 'AI' technologies in universities. As an educational institution, we cannot condone the uncritical use of AI by students, faculty, or leadership...It undermines our basic pedagogical values and the principles of scientific integrity. It prevents us from maintaining our standards of independence and transparency. And most concerning, AI use has been shown to hinder learning and deskill critical thought'

#pedagogy #coercivePlatforms #education #criticalThinking #technology

https://openletter.earth/open-letter-stop-the-uncritical-adoption-of-ai-technologies-in-academia-b65bba1e

'With this letter we take a principled stand against the proliferation of so-called 'AI' technologies in universities. As an educational institution, we cannot condone the uncritical use of AI by students, faculty, or leadership...It undermines our basic pedagogical values and the principles of scientific integrity. It prevents us from maintaining our standards of independence and transparency. And most concerning, AI use has been shown to hinder learning and deskill critical thought'

#pedagogy #coercivePlatforms #education #criticalThinking #technology

https://openletter.earth/open-letter-stop-the-uncritical-adoption-of-ai-technologies-in-academia-b65bba1e

'With this letter we take a principled stand against the proliferation of so-called 'AI' technologies in universities. As an educational institution, we cannot condone the uncritical use of AI by students, faculty, or leadership...It undermines our basic pedagogical values and the principles of scientific integrity. It prevents us from maintaining our standards of independence and transparency. And most concerning, AI use has been shown to hinder learning and deskill critical thought'

#pedagogy #coercivePlatforms #education #criticalThinking #technology

https://openletter.earth/open-letter-stop-the-uncritical-adoption-of-ai-technologies-in-academia-b65bba1e

'With this letter we take a principled stand against the proliferation of so-called 'AI' technologies in universities. As an educational institution, we cannot condone the uncritical use of AI by students, faculty, or leadership...It undermines our basic pedagogical values and the principles of scientific integrity. It prevents us from maintaining our standards of independence and transparency. And most concerning, AI use has been shown to hinder learning and deskill critical thought'

#pedagogy #coercivePlatforms #education #criticalThinking #technology

https://openletter.earth/open-letter-stop-the-uncritical-adoption-of-ai-technologies-in-academia-b65bba1e

'With this letter we take a principled stand against the proliferation of so-called 'AI' technologies in universities. As an educational institution, we cannot condone the uncritical use of AI by students, faculty, or leadership...It undermines our basic pedagogical values and the principles of scientific integrity. It prevents us from maintaining our standards of independence and transparency. And most concerning, AI use has been shown to hinder learning and deskill critical thought'

#pedagogy #coercivePlatforms #education #criticalThinking #technology

https://openletter.earth/open-letter-stop-the-uncritical-adoption-of-ai-technologies-in-academia-b65bba1e

Why prompting should not be our focus when teaching students about AI

This is a sketch of a much longer piece of work I need to start:

• The role of prompting is diminishing as chatbots develop because they increasingly infer and pre-empt what the user shares. It’s no longer a crucial skill for using them effectively and we need to avoid presenting it as if it is.
• As Essien et al put it, “agency in prompting does not automatically produce learning; without reflective regulation, it may become only efficient prompting”. The student might still treat uncritically accept outputs, outsource to a self-defeating degree or engage in malpractice while nonetheless prompting effectively.
• It’s hard to talk about prompting without veering into the terrain of ‘prompt engineering’. This is a contingent feature of the discourse but there’s a whole theory of what models are implicit in the notion of ‘prompt engineering’ which is really unhelpful for learning: imagining prompting as a form of natural language coding through which you unlock the capabilities of the machine.
• The language of prompting also lends itself to thinking about single-shot interaction which is not how you use a chatbot in reflective and thoughtful ways. Again there’s nothing necessary about this but the discourse constrains our capacity to emphasise the dialogical element as essential while still talking about prompting.
• If we see the use of chatbots for learning in terms of dialogue, then we need a dialogical vocabulary for thinking about it. What makes for a better or worse conversation? What is the student bringing to the model? How are they greeting the responses of the chatbot? How does the interaction unfold through these dynamics over time? This is the terrain we need to work on and again I think the language gets in the way.
• The capabilities involved in this are much more like literacy more broadly than the language of prompting can really account for. To write fluently, confidently and thoughtfully equips you to prompt effectively. To draw upon a range of references in expansive and relevant ways unlocks the capability of the model to respond to complexity. If we talk about this as ‘prompting’ we redescribe what are relatively familiar aspirations of humanistic education in an unhelpful way. This also obscures inequalities of cultural capital in who can do this and who cannot do it.

I realise in writing this my problem is less with prompting and more on prioritising it and the conceptual baggage which comes with it. Instead I suggest the focus needs to be on student agency when using chatbots. Essien et al again:

When students maintain control over goals, prompts, evaluation, and final judgement, the feared risk of cognitive atrophy may be reduced (Yang et al. 2024), and AI may function less as a surrogate and more as a dialogic partner (Krakowski 2025). This aligns with warnings that passive AI use may weaken learning (Linde- baum et al. 2025).

Why prompting should not be our focus when teaching students about AI

This is a sketch of a much longer piece of work I need to write:

• The role of prompting is diminishing is diminishing as chatbots develop because they increasingly infer and pre-empt what the user shares. It’s no longer a crucial skill for using them effectively and we need to avoid presenting it as if it is.
• As Essien et al put it, “agency in prompting does not automatically produce learning; without reflective regulation, it may become only efficient prompting”. The student might still treat uncritically accept outputs, outsource to a self-defeating degree or engage in malpractice while nonetheless prompting effectively.
• It’s hard to talk about prompting without veering into the terrain of ‘prompt engineering’. This is a contingent feature of the discourse but there’s a whole theory of what models are implicit in the notion of ‘prompt engineering’ which is really unhelpful for learning: imagining prompting as a form of natural language coding through which you unlock the machine.
• The language of prompting also lends itself to thinking about single-shot interaction which is now how you use a chatbot in reflective and thoughtful ways. Again there’s nothing necessary about this but the discourse constraints our capacity to emphasise the dialogical element as essential while still talking about prompting.
• If we see the use of chatbots for learning in terms of dialogue, then we need a dialogical vocabulary for thinking about it. What makes for a better or worse conversation? What is the student bringing to the model? How are they greeting the responses of the chatbot? How does the interaction unfold through these dynamics over time? This is the terrain we need to work on and again I think the language gets in the way.
• The capabilities involved in this are much more like literacy more broadly than the language of prompting can really account for. To write fluently, confidently and thoughtfully equips you to prompt effectively. To draw upon a range of references in expansive and relevant ways unlocks the capability of the model to respond to complexity. If we talk about this as ‘prompting’ we redescribe what are relatively familiar aspirations of humanistic education in an unhelpful way. This also obscures inequalities of cultural capital in who can do this and who cannot do it.

I realise in writing this my problem is less with prompting and more on prioritising it and the conceptual baggage which comes with it. Instead I suggest the focus needs to be on student agency when using chatbots. Essien et al again:

When students maintain control over goals, prompts, evaluation, and final judgement, the feared risk of cognitive atrophy may be reduced (Yang et al. 2024), and AI may function less as a surrogate and more as a dialogic partner (Krakowski 2025). This aligns with warnings that passive AI use may weaken learning (Linde- baum et al. 2025).

So perhaps I’d see student agency as composed of (a) prompting skills (b) literacy (c) critical AI literacy?

Why prompting should not be our focus when teaching students about AI

This is a sketch of a much longer piece of work I need to start:

• The role of prompting is diminishing as chatbots develop because they increasingly infer and pre-empt what the user shares. It’s no longer a crucial skill for using them effectively and we need to avoid presenting it as if it is.
• As Essien et al put it, “agency in prompting does not automatically produce learning; without reflective regulation, it may become only efficient prompting”. The student might still treat uncritically accept outputs, outsource to a self-defeating degree or engage in malpractice while nonetheless prompting effectively.
• It’s hard to talk about prompting without veering into the terrain of ‘prompt engineering’. This is a contingent feature of the discourse but there’s a whole theory of what models are implicit in the notion of ‘prompt engineering’ which is really unhelpful for learning: imagining prompting as a form of natural language coding through which you unlock the capabilities of the machine.
• The language of prompting also lends itself to thinking about single-shot interaction which is not how you use a chatbot in reflective and thoughtful ways. Again there’s nothing necessary about this but the discourse constrains our capacity to emphasise the dialogical element as essential while still talking about prompting.
• If we see the use of chatbots for learning in terms of dialogue, then we need a dialogical vocabulary for thinking about it. What makes for a better or worse conversation? What is the student bringing to the model? How are they greeting the responses of the chatbot? How does the interaction unfold through these dynamics over time? This is the terrain we need to work on and again I think the language gets in the way.
• The capabilities involved in this are much more like literacy more broadly than the language of prompting can really account for. To write fluently, confidently and thoughtfully equips you to prompt effectively. To draw upon a range of references in expansive and relevant ways unlocks the capability of the model to respond to complexity. If we talk about this as ‘prompting’ we redescribe what are relatively familiar aspirations of humanistic education in an unhelpful way. This also obscures inequalities of cultural capital in who can do this and who cannot do it.

I realise in writing this my problem is less with prompting and more on prioritising it and the conceptual baggage which comes with it. Instead I suggest the focus needs to be on student agency when using chatbots. Essien et al again:

When students maintain control over goals, prompts, evaluation, and final judgement, the feared risk of cognitive atrophy may be reduced (Yang et al. 2024), and AI may function less as a surrogate and more as a dialogic partner (Krakowski 2025). This aligns with warnings that passive AI use may weaken learning (Linde- baum et al. 2025).

Why prompting should not be our focus when teaching students about AI

This is a sketch of a much longer piece of work I need to start:

• The role of prompting is diminishing as chatbots develop because they increasingly infer and pre-empt what the user shares. It’s no longer a crucial skill for using them effectively and we need to avoid presenting it as if it is.
• As Essien et al put it, “agency in prompting does not automatically produce learning; without reflective regulation, it may become only efficient prompting”. The student might still treat uncritically accept outputs, outsource to a self-defeating degree or engage in malpractice while nonetheless prompting effectively.
• It’s hard to talk about prompting without veering into the terrain of ‘prompt engineering’. This is a contingent feature of the discourse but there’s a whole theory of what models are implicit in the notion of ‘prompt engineering’ which is really unhelpful for learning: imagining prompting as a form of natural language coding through which you unlock the capabilities of the machine.
• The language of prompting also lends itself to thinking about single-shot interaction which is not how you use a chatbot in reflective and thoughtful ways. Again there’s nothing necessary about this but the discourse constrains our capacity to emphasise the dialogical element as essential while still talking about prompting.
• If we see the use of chatbots for learning in terms of dialogue, then we need a dialogical vocabulary for thinking about it. What makes for a better or worse conversation? What is the student bringing to the model? How are they greeting the responses of the chatbot? How does the interaction unfold through these dynamics over time? This is the terrain we need to work on and again I think the language gets in the way.
• The capabilities involved in this are much more like literacy more broadly than the language of prompting can really account for. To write fluently, confidently and thoughtfully equips you to prompt effectively. To draw upon a range of references in expansive and relevant ways unlocks the capability of the model to respond to complexity. If we talk about this as ‘prompting’ we redescribe what are relatively familiar aspirations of humanistic education in an unhelpful way. This also obscures inequalities of cultural capital in who can do this and who cannot do it.

I realise in writing this my problem is less with prompting and more on prioritising it and the conceptual baggage which comes with it. Instead I suggest the focus needs to be on student agency when using chatbots. Essien et al again:

When students maintain control over goals, prompts, evaluation, and final judgement, the feared risk of cognitive atrophy may be reduced (Yang et al. 2024), and AI may function less as a surrogate and more as a dialogic partner (Krakowski 2025). This aligns with warnings that passive AI use may weaken learning (Linde- baum et al. 2025).

Why prompting should not be our focus when teaching students about AI

This is a sketch of a much longer piece of work I need to start:

• The role of prompting is diminishing as chatbots develop because they increasingly infer and pre-empt what the user shares. It’s no longer a crucial skill for using them effectively and we need to avoid presenting it as if it is.
• As Essien et al put it, “agency in prompting does not automatically produce learning; without reflective regulation, it may become only efficient prompting”. The student might still treat uncritically accept outputs, outsource to a self-defeating degree or engage in malpractice while nonetheless prompting effectively.
• It’s hard to talk about prompting without veering into the terrain of ‘prompt engineering’. This is a contingent feature of the discourse but there’s a whole theory of what models are implicit in the notion of ‘prompt engineering’ which is really unhelpful for learning: imagining prompting as a form of natural language coding through which you unlock the capabilities of the machine.
• The language of prompting also lends itself to thinking about single-shot interaction which is not how you use a chatbot in reflective and thoughtful ways. Again there’s nothing necessary about this but the discourse constrains our capacity to emphasise the dialogical element as essential while still talking about prompting.
• If we see the use of chatbots for learning in terms of dialogue, then we need a dialogical vocabulary for thinking about it. What makes for a better or worse conversation? What is the student bringing to the model? How are they greeting the responses of the chatbot? How does the interaction unfold through these dynamics over time? This is the terrain we need to work on and again I think the language gets in the way.
• The capabilities involved in this are much more like literacy more broadly than the language of prompting can really account for. To write fluently, confidently and thoughtfully equips you to prompt effectively. To draw upon a range of references in expansive and relevant ways unlocks the capability of the model to respond to complexity. If we talk about this as ‘prompting’ we redescribe what are relatively familiar aspirations of humanistic education in an unhelpful way. This also obscures inequalities of cultural capital in who can do this and who cannot do it.

I realise in writing this my problem is less with prompting and more on prioritising it and the conceptual baggage which comes with it. Instead I suggest the focus needs to be on student agency when using chatbots. Essien et al again:

When students maintain control over goals, prompts, evaluation, and final judgement, the feared risk of cognitive atrophy may be reduced (Yang et al. 2024), and AI may function less as a surrogate and more as a dialogic partner (Krakowski 2025). This aligns with warnings that passive AI use may weaken learning (Linde- baum et al. 2025).

The Case for Aesthetic Education

Why Friedrich Schiller believed art belongs at the heart of education—and why it matters for equal opportunity today.

By: Marco Elvis Joven Domínguez

https://daily.jstor.org/the-case-for-aesthetic-education/

Books by Friedrich Schiller at PG:
https://www.gutenberg.org/ebooks/author/289

#books #literature #pedagogy

The Case for Aesthetic Education

Why Friedrich Schiller believed art belongs at the heart of education—and why it matters for equal opportunity today.

By: Marco Elvis Joven Domínguez

https://daily.jstor.org/the-case-for-aesthetic-education/

Books by Friedrich Schiller at PG:
https://www.gutenberg.org/ebooks/author/289

#books #literature #pedagogy

The Case for Aesthetic Education

Why Friedrich Schiller believed art belongs at the heart of education—and why it matters for equal opportunity today.

By: Marco Elvis Joven Domínguez

https://daily.jstor.org/the-case-for-aesthetic-education/

Books by Friedrich Schiller at PG:
https://www.gutenberg.org/ebooks/author/289

#books #literature #pedagogy

The Case for Aesthetic Education

Why Friedrich Schiller believed art belongs at the heart of education—and why it matters for equal opportunity today.

By: Marco Elvis Joven Domínguez

https://daily.jstor.org/the-case-for-aesthetic-education/

Books by Friedrich Schiller at PG:
https://www.gutenberg.org/ebooks/author/289

#books #literature #pedagogy

The Case for Aesthetic Education

Why Friedrich Schiller believed art belongs at the heart of education—and why it matters for equal opportunity today.

By: Marco Elvis Joven Domínguez

https://daily.jstor.org/the-case-for-aesthetic-education/

Books by Friedrich Schiller at PG:
https://www.gutenberg.org/ebooks/author/289

#books #literature #pedagogy

@henkheijmans

#altText : a vintage #classroom, with #boys only but with some unusual aspects: pulpits are not oriented towards the blackboard (as they still generally are in #France except in #kindergarten #school) but facing one another, there's a "News" board full of pinned sheets and objects, and a table (presumably the unseen teacher's) is covered with pots where plants appear to be growing.

1/2

https://fr.wikipedia.org/wiki/P%C3%A9dagogie_Freinet

#education #teaching #école #Freinet #pédagogie #pedagogy

@henkheijmans

#altText : a vintage #classroom, with #boys only but with some unusual aspects: pulpits are not oriented towards the blackboard (as they still generally are in #France except in #kindergarten #school) but facing one another, there's a "News" board full of pinned sheets and objects, and a table (presumably the unseen teacher's) is covered with pots where plants appear to be growing.

1/2

https://fr.wikipedia.org/wiki/P%C3%A9dagogie_Freinet

#education #teaching #école #Freinet #pédagogie #pedagogy

@henkheijmans

#altText : a vintage #classroom, with #boys only but with some unusual aspects: pulpits are not oriented towards the blackboard (as they still generally are in #France except in #kindergarten #school) but facing one another, there's a "News" board full of pinned sheets and objects, and a table (presumably the unseen teacher's) is covered with pots where plants appear to be growing.

1/2

https://fr.wikipedia.org/wiki/P%C3%A9dagogie_Freinet

#education #teaching #école #Freinet #pédagogie #pedagogy

@henkheijmans

#altText : a vintage #classroom, with #boys only but with some unusual aspects: pulpits are not oriented towards the blackboard (as they still generally are in #France except in #kindergarten #school) but facing one another, there's a "News" board full of pinned sheets and objects, and a table (presumably the unseen teacher's) is covered with pots where plants appear to be growing.

1/2

https://fr.wikipedia.org/wiki/P%C3%A9dagogie_Freinet

#education #teaching #école #Freinet #pédagogie #pedagogy

@henkheijmans

#altText : a vintage #classroom, with #boys only but with some unusual aspects: pulpits are not oriented towards the blackboard (as they still generally are in #France except in #kindergarten #school) but facing one another, there's a "News" board full of pinned sheets and objects, and a table (presumably the unseen teacher's) is covered with pots where plants appear to be growing.

1/2

https://fr.wikipedia.org/wiki/P%C3%A9dagogie_Freinet

#education #teaching #école #Freinet #pédagogie #pedagogy

RE: https://scholar.social/@olivia/117026104474024967

‘The role of the university is even lost on many, so bears repeating: produce knowledge and educate at the highest level. AI destroys both at their core. It exposes some of us as confused, some of us as opportunistic, and both groups as cowards who would rather extract than reflect, would rather move fast and break things than learn to follow the slow and methodical praxis of scholarly work’

#pedagogy #academicChatter

RE: https://scholar.social/@olivia/117026104474024967

‘The role of the university is even lost on many, so bears repeating: produce knowledge and educate at the highest level. AI destroys both at their core. It exposes some of us as confused, some of us as opportunistic, and both groups as cowards who would rather extract than reflect, would rather move fast and break things than learn to follow the slow and methodical praxis of scholarly work’

#pedagogy #academicChatter

RE: https://scholar.social/@olivia/117026104474024967

‘The role of the university is even lost on many, so bears repeating: produce knowledge and educate at the highest level. AI destroys both at their core. It exposes some of us as confused, some of us as opportunistic, and both groups as cowards who would rather extract than reflect, would rather move fast and break things than learn to follow the slow and methodical praxis of scholarly work’

#pedagogy #academicChatter

RE: https://scholar.social/@olivia/117026104474024967

‘The role of the university is even lost on many, so bears repeating: produce knowledge and educate at the highest level. AI destroys both at their core. It exposes some of us as confused, some of us as opportunistic, and both groups as cowards who would rather extract than reflect, would rather move fast and break things than learn to follow the slow and methodical praxis of scholarly work’

#pedagogy #academicChatter

RE: https://scholar.social/@olivia/117026104474024967

‘The role of the university is even lost on many, so bears repeating: produce knowledge and educate at the highest level. AI destroys both at their core. It exposes some of us as confused, some of us as opportunistic, and both groups as cowards who would rather extract than reflect, would rather move fast and break things than learn to follow the slow and methodical praxis of scholarly work’

#pedagogy #academicChatter

https://ergosphere.blog/posts/the-machines-are-fine/

"[Frank Herbert] has a character observe: 'What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger.' Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small."

This post by ergosphere.blog gets at what bothers me about LLMs.

#LLM #AI #programming #pedagogy #learning

https://ergosphere.blog/posts/the-machines-are-fine/

"[Frank Herbert] has a character observe: 'What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger.' Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small."

This post by ergosphere.blog gets at what bothers me about LLMs.

#LLM #AI #programming #pedagogy #learning

https://ergosphere.blog/posts/the-machines-are-fine/

"[Frank Herbert] has a character observe: 'What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger.' Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small."

This post by ergosphere.blog gets at what bothers me about LLMs.

#LLM #AI #programming #pedagogy #learning

https://ergosphere.blog/posts/the-machines-are-fine/

"[Frank Herbert] has a character observe: 'What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger.' Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small."

This post by ergosphere.blog gets at what bothers me about LLMs.

#LLM #AI #programming #pedagogy #learning

https://ergosphere.blog/posts/the-machines-are-fine/

"[Frank Herbert] has a character observe: 'What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger.' Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small."

This post by ergosphere.blog gets at what bothers me about LLMs.

#LLM #AI #programming #pedagogy #learning

Too many events still use child-based pedagogical instead of adult-centered andragogical modalities. Concentrate on the latter.

https://www.conferencesthatwork.com/index.php/learning/2015/06/three-key-kinds-of-learning-at-events-part-2

#meetings #events #EventDesign #learning #andragogy #pedagogy #eventprofs

Too many events still use child-based pedagogical instead of adult-centered andragogical modalities. Concentrate on the latter.

https://www.conferencesthatwork.com/index.php/learning/2015/06/three-key-kinds-of-learning-at-events-part-2

#meetings #events #EventDesign #learning #andragogy #pedagogy #eventprofs

Too many events still use child-based pedagogical instead of adult-centered andragogical modalities. Concentrate on the latter.

https://www.conferencesthatwork.com/index.php/learning/2015/06/three-key-kinds-of-learning-at-events-part-2

#meetings #events #EventDesign #learning #andragogy #pedagogy #eventprofs

Too many events still use child-based pedagogical instead of adult-centered andragogical modalities. Concentrate on the latter.

https://www.conferencesthatwork.com/index.php/learning/2015/06/three-key-kinds-of-learning-at-events-part-2

#meetings #events #EventDesign #learning #andragogy #pedagogy #eventprofs

Too many events still use child-based pedagogical instead of adult-centered andragogical modalities. Concentrate on the latter.

https://www.conferencesthatwork.com/index.php/learning/2015/06/three-key-kinds-of-learning-at-events-part-2

#meetings #events #EventDesign #learning #andragogy #pedagogy #eventprofs

'Stanford’s “The Evidence Base on AI in K-12” reviewed over 800 academic papers and found exactly 20 that produced strong causal evidence. Twenty. Out of 800...The hot take that AI will transform education is running about 780 papers ahead of the evidence'

#pedagogy #polycrisis #education #genAI

https://medium.com/@jasonsattler/heres-what-we-know-about-ai-and-education-it-s-not-good-672340aa728f

'Stanford’s “The Evidence Base on AI in K-12” reviewed over 800 academic papers and found exactly 20 that produced strong causal evidence. Twenty. Out of 800...The hot take that AI will transform education is running about 780 papers ahead of the evidence'

#pedagogy #polycrisis #education #genAI

https://medium.com/@jasonsattler/heres-what-we-know-about-ai-and-education-it-s-not-good-672340aa728f

'Stanford’s “The Evidence Base on AI in K-12” reviewed over 800 academic papers and found exactly 20 that produced strong causal evidence. Twenty. Out of 800...The hot take that AI will transform education is running about 780 papers ahead of the evidence'

#pedagogy #polycrisis #education #genAI

https://medium.com/@jasonsattler/heres-what-we-know-about-ai-and-education-it-s-not-good-672340aa728f

'Stanford’s “The Evidence Base on AI in K-12” reviewed over 800 academic papers and found exactly 20 that produced strong causal evidence. Twenty. Out of 800...The hot take that AI will transform education is running about 780 papers ahead of the evidence'

#pedagogy #polycrisis #education #genAI

https://medium.com/@jasonsattler/heres-what-we-know-about-ai-and-education-it-s-not-good-672340aa728f

'Stanford’s “The Evidence Base on AI in K-12” reviewed over 800 academic papers and found exactly 20 that produced strong causal evidence. Twenty. Out of 800...The hot take that AI will transform education is running about 780 papers ahead of the evidence'

#pedagogy #polycrisis #education #genAI

https://medium.com/@jasonsattler/heres-what-we-know-about-ai-and-education-it-s-not-good-672340aa728f

Poll: Please boot for reach

Premised on the notion of evidence based policy and practice, if we accept that substantive critical reviews have found that research demonstrates EdTech to be of very limited benefit in secondary education (and often somewhat detrimental), do educators and departments of education have a duty to structure curricula and approved materials to exclude use of EdTech outside of explicitly CompSci oriented subject matter?

#technology #pedagogy #teachingAndLearning #lifeSkills

Poll: Please boot for reach

Premised on the notion of evidence based policy and practice, if we accept that substantive critical reviews have found that research demonstrates EdTech to be of very limited benefit in secondary education (and often somewhat detrimental), do educators and departments of education have a duty to structure curricula and approved materials to exclude use of EdTech outside of explicitly CompSci oriented subject matter?

#technology #pedagogy #teachingAndLearning #lifeSkills

Poll: Please boot for reach

Premised on the notion of evidence based policy and practice, if we accept that substantive critical reviews have found that research demonstrates EdTech to be of very limited benefit in secondary education (and often somewhat detrimental), do educators and departments of education have a duty to structure curricula and approved materials to exclude use of EdTech outside of explicitly CompSci oriented subject matter?

#technology #pedagogy #teachingAndLearning #lifeSkills