Raiding the inarticulate since 2010

accelerated academy acceleration agency AI Algorithmic Authoritarianism and Digital Repression archer Archive Archiving artificial intelligence automation Becoming Who We Are Between Post-Capitalism and Techno-Fascism big data blogging capitalism ChatGPT claude Cognitive Triage: Practice, Culture and Strategies Communicative Escalation and Cultural Abundance: How Do We Cope? Corporate Culture, Elites and Their Self-Understandings craft creativity critical realism data science Defensive Elites desire Digital Capitalism and Digital Social Science Digital Distraction, Personal Agency and The Reflexive Imperative Digital Elections, Party Politics and Diplomacy digital elites Digital Inequalities Digital Social Science Digital Sociology digital sociology Digital Universities elites Fragile Movements and Their Politics Cultures generative AI higher education Interested labour Lacan Listening LLMs margaret archer Organising personal morphogenesis Philosophy of Technology platform capitalism platforms populism Post-Democracy, Depoliticisation and Technocracy post-truth psychoanalysis public engagement public sociology publishing Reading realism reflexivity scholarship Shadow Mobilization, Astroturfing and Manipulation Social Media Social Media for Academics social media for academics social ontology social theory sociology technology The Content Ecosystem The Intensification of Work The Political Economy of Digital Capitalism The Technological History of Digital Capitalism Thinking trump twitter Uncategorized work writing zizek

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.

, , , ,
,