#metacognition — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #metacognition, aggregated by home.social.
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Podcast News, le média de l’industrie francophone du podcast, s’est fait l’écho de mon appel du week-end dernier pour sauver Méta de Choc, suite à la chute dramatique des dons que subit le podcast depuis le début de cette année 2026 (voir liens en fin de thread). 1/5
#métadechoc #dons #soutien #podcastfr #EspritCritique #scepticisme #métacognition #PenséeCritique
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Méta de Choc survivra-t-il à 2026 ?
La question s’impose malheureusement.
Il y a 7 ans, j’ai décidé de lancer ce podcast indépendant, gratuit et sans pub, exclusivement financé par ses auditeur•ices. Simple, n’est-ce pas ? Et pour me rendre la tâche encore plus facile, j’ai choisi un sujet ultra-mainstream : la métacognition. 1/9
#podcast #SansPub #scepticisme #métacognition #EspritCritique #don
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Socrates worried that writing would damage our memories. We only know about his concern because somebody wrote it down.
Every generation runs this argument. But "people have always worried" is not the same as "there is nothing to worry about". Generative AI can now produce the work itself, not just store it.
The question worth asking is which cognitive work you want to keep doing: https://www.ctnet.co.uk/cognitive-debt-ai-offloading/
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Practice what you teach. Because teachings don't function as symbols or metaphors…they are incarnations of what they advocate.
https://www.conferencesthatwork.com/index.php/learning/2017/05/practice-what-you-teach
#learning #teaching #practice #RecursivePractice #MetaCognition
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A line from my latest post that I keep coming back to: AI's neural network is fixed once trained - ours isn't. We evolved to adapt and learn throughout life, it's still one of the few edges we have left.
Full post: https://www.ctnet.co.uk/protect-cognitive-abilities-ai-age/
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A tip from my latest post: metacognition (thinking about your own thinking) is a meta-skill worth building deliberately.
Three ways in: self-testing, self-explanation, and concept mapping. None of them are complicated, they just need doing consistently.
More here: https://www.ctnet.co.uk/protect-cognitive-abilities-ai-age/
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The final post in my "Why Technology Is Outpacing Society" series is live.
After four posts on why the gap between tech and us keeps widening, I didn't want to end on doom. So this one's about what's actually within our control: protecting our own cognitive abilities.
Read it here: https://www.ctnet.co.uk/protect-cognitive-abilities-ai-age/
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Détective privée : en quête de vérité 1/3, avec Margaux Duquesne — SHOCKING #29
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#AiTip : Level up your AI prompting AND reasoning.
Rather than asking:
"What's the answer?"Ask:
"Which reasoning strategy should I use?" -
Healing Trauma!
Unlock the secrets to healing trauma! Explore how emotional coherence and metacognition can help you process difficult experiences and transform your identity. Learn how long to feel emotions and when to move forward.
#traumahealing #emotionalhealing #personalgrowth #metacognition #mindfulness #mentalhealth #selfhelp #NeuroFeedback #Neuropathy #NeuroSurgeon #NeuroDiversidade #NeuroPsychology #NeuroSciences #Neurologie #NeuroCoaching #NeuroRehab #wellbeing #innerpeace
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What are the conditions which make it possible to learn with AI?
This thoughtful pre-print by Favero et al offered a pleasingly straight forward answer to this question. Ultimately we know what makes for effective learning:
- It has to involve active engagement in which the learner is reflecting, connecting and synthesising material rather than simply passively receiving it.
- It has to integrate the new understanding which emerges into their existing understanding through direct experience and dialogue.
- It has to involve the phased withdrawal of support as learners gain competence and confidence.
Rather than ask ‘what are the conditions which make it possible to learn with AI’ we can instead ask ‘what are the conditions which make it possible to use AI in these ways‘? The problem with commercial chatbots is not the technology itself but rather the design decisions involved in making a technology for a mass audience (as Nick Srnicek has plausibly argued AGI is ultimately an ambition to create a product which work universally without sector-specific fine tuning) which mean that, not only is it not adapted to these specific educational requirements, it actively works against them in a number of ways:
- The role of prompting has become decreasingly important with successive models
- The models are increasingly agentive in the sense of able and willing to actively do stuff for the user
- The role of model memory makes it easier for habits to form over time so that users get locked into certain ways of using the chatbot
This means there’s a fundamental tension in using these chatbots for educational purposes. It doesn’t mean it’s impossible. We also shouldn’t look the perfect be the enemy of the good: as the WonkHE report plausibly argued their widespread use reflects weaknesses of existing provision in that students are drawing on them to meet needs we are failing to meet. But we need to be realistic about the underlying tension, even as we try and mitigate it through AI literacy. In part this means helping students relate to chatbots in a way which doesn’t avoid difficulty. I really like how the authors describe the problem here:
However, learners often try to avoid such an effort. Studies show that high perceived difficulty and low short-term performance can discourage engagement, despite clear long- term benefits. Interestingly, Deslauriers et al. [29] found that students in active learning environments learned more but felt they learned less. Mental effort was misinterpreted as failure, while smooth lectures mistakenly felt more effective, though they were not. This cognitive bias leads students to favor fluent, low-effort activities that give the illusion of learning, such as re-reading polished explanations, without fostering deep processing [27, 30, 16].
AI tools, and particularly chatbots, may exacerbate this issue. By offering quick, fluent, and simplified answers, they reduce the cognitive struggle which is essential to learning [14]. Their convenience may lead to passive consumption, decreased research and reasoning skills, and a growing dependence on pre-digested knowledge [31]. Ease of use is appealing, but true learning comes from effort, complexity, and time.
This isn’t just significant for their studies. There’s AI slop proliferating in academic workplaces which appear to embody the same tendency, in which people in a rush can produce something superficially plausible which lets them tick it off a list and leaves them feeling more accomplished. Knowing how to sit with difficulty, to avoid the temptation of superficially fluent outputs, will I’m fairly confident be a skill that employers will value ever more in the coming years. This means recognising the difference between ‘desirable difficulties’ and contingent barriers which can be automated away. It’s impossible to do this unless use remains active throughout what Milan Sturmer and I call the user-model interaction cycle:
- Positioning: establishing a role for the model in interaction
- Articulation: putting ideas into words which are provided by the model
- Attunement: feeling the model has ‘understood’ what the user has brought to the interaction
Each of these dimensions can be (relatively) passive. Each of these dimensions can be active. The thread uniting active use is metacognition, in the sense the authors talk about here:
Routine use of AI tools can hinder the development of metacognitive skills, independent thinking, and intellectual agency [5, 12]. As students begin to outsource decision-making to the ma- chine, they risk becoming passive recipients of information rather than critical participants in the learning process [4]. This passivity is linked to broader deficits, including reduced creativity, increased mental laziness, and diminished capacity for critical thought [16]. Moreover, dependency on AI tools can lead to the uncritical acceptance of their outputs. When students perceive these systems as convenient, accurate, and reliable, they may stop questioning the information provided, which fosters cognitive dependency, i.e., the erosion of the ability to assess, verify, and challenge content independently
We need to help students name what it feels like to be actively thinking when interacting with a model. There’s a distinct phenomenology to this. It’s also something which cannot be sustained indefinitely. As you get tired, it’s easy to slide into increasingly passive uses of the model, with ever more capable models almost seamlessly picking up the load from you.
#activeLearning #AI #constructivism #effectiveLearning #metacognition -
Generative AI and metacognitive laziness
While I’m sceptical of their experiment research design*, the concept of metacognitive laziness from this paper is clearly a useful contribution to thel literature. As Fan et al define it, this refers to “earners’ dependence on AI assistance, offloading meta – cognitive load and less effectively associating responsible metacognitive processes with learning tasks”. This matters because “offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks,” (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.
This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:
the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners’ ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.
The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesn’t just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the ‘cognitive offloading’ concept.
*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the student’s expressed engagement with discrete tasks then it’s been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like ‘cognitive off-loading’, ‘skill atrophy’ and ‘metacognitive laziness’. These just aren’t things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.
#AI #cognitiveOffloading #cognitiveScience #learning #metacognition #selfDirectedLearning #Thinking -
Pluralistic: Daily links from Cory Doctorow – No trackers, no ads. Black type, white background. Privacy policy: we don't collect or retain any data at all ever period. [Unofficial] @[email protected] ·Pluralistic: Refining humanity (05 Jun 2026)
https://web.brid.gy/r/https://pluralistic.net/2026/06/05/defining-humanity/
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Sometimes, I feel like I spend too much time in metacognition 🤪
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10 posts about #metacognition -- a vital skill in the age of social media and #ai
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Teaching #metacognition techniques to 4-6 year olds leads to better learning outcomes (and may immunize against cognitive decline caused by #ai use)
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I used chatGPT to research cognitive risks of undisciplined use of #AI and what to do about it, then created a series of 7 books for my 5 year old grandson. If you don't want to download the 60 mb PDF of the illustrated books, this detailed curriculum guide details the pedagogy of #metacognition for very young people
https://rheingold.com/READ_FIRST_Thinking_System_Curriculum_Intro.pdf
and the entire set of books
https://rheingold.com/ThinkingCurriculum.zip -
Les croyances dans le sport 1/6, avec Willy Mangin — SHOCKING #30
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Practice what you teach. Because teachings don't function as symbols or metaphors…they are incarnations of what they advocate.
https://www.conferencesthatwork.com/index.php/learning/2017/05/practice-what-you-teach
#learning #teaching #practice #RecursivePractice #MetaCognition
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Détective privée : en quête de vérité 1/3, avec Margaux Duquesne — SHOCKING #29
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Ça fait 2 ans et 10 mois que je ne vous avais pas proposé de série SHOCKING ! Vous savez, ces #entretiens au long cours dans lesquels j’échange avec, soit une personne qui a questionné en profondeur ses croyances, soit un•e expert•e qui apporte un éclairage inédit sur la manière dont les humains pensent.
⏯️ Teaser : https://youtu.be/8fid2jxRkLw
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5 modèles d'apprentissage avec l'IA qui introduisent des biais #metacognition Par Roger Azevedo University of Central Florida extrait d'une conférence #csen https://www.youtube.com/watch?v=ITtLW9U3yFI&list=PLkPlJ-yhH0ITLHnrMC5SgmvzrlsFf0LxF&index=6
