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What is the problem to which cognitive outsourcing is the solution?

This paper by Thomas Corbin et al reports on a pilot study of philosophy undergraduates exploring their use of AI-reading tools. Their analysis of half of students using generative AI tools in some way for reading. Interestingly, the vast majority (79.1%) recognised the importance of this reading while also citing limited time (65.7%) and intellectually difficulty (33.3%) with the texts. They suggest a positive trend underlying the familiar fears about cognitive outsourcing. From pg 6:

The strong positive sentiment toward GenAI availability (76.2%) suggests these tools are making students more comfortable with challenging content, potentially lowering anxiety barriers to engagement with complex reading material. By providing alternative entry points to challenging texts, GenAI tools may help democratise access, particularly for students who face epistemic barriers to traditional engagement with reading materials. However, this optimistic interpretation must be balanced against potential risks. While GenAI may help students overcome initial barriers, over-reliance on AI-generated summaries could potentially impede the development of critical reading and interpretive skills that are essential to philosophical education.

This is what I mean about the need to respond diagnostically to student AI use. There are real problems in teaching and learning being surfaced by developing trends in student AI-use. What is the problem to which cognitive outsourcing is the solution for students? In asking this question it becomes possible to diagnose the underlying challenges which pre-existed generative AI, as well as to better understand student use in a manner which enables us to steer them towards active rather than passive use of AI.

This is a way of approaching student practice which enables us to surface difficulties. It still leaves us with the question though of which difficulties are undesirable obstacles and which difficulties are constitutive challenges. What do students need to work through in order to learn (and how do we help them with this?) versus what aspects of teaching and learning get in the way and should potentially be dispensed with? Is this part of the solution to my overarching question of what it means for students to use AI in active rather than passive ways?

Who can authoritatively judge whether a difficult falls is undesirable or constitutive? I think it has to be disciplinary-based expertise. If you don’t keep the link with disciplinary expertise then you can’t solve the problems of generative AI. That at least is the conclusion I’m rapidly coming to, which I’ll explore in future posts in this series.

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