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The ethical reasoning of students about AI is an untapped resource

I thought this was an incredibly thought-provoking framing in WonkHe’s recent report concerning how students are already deliberating about AI in complex and sophisticated way. The incoherence of the sector response to these challenges has forced students to deliberate about them in ways which are often complex and sophisticated:

But many students have gone further than working through confusing policy. They are constructing sophisticated ethical frameworks of their own – often more considered than anything their institution has produced. A computer science student described his governing principle: “I make sure my use of AI doesn’t inhibit my understanding of the topic. For essay submissions, the final text is written by myself – I don’t want to lose the ability to report on my findings.” An engineering student had arrived at a principle of “augmentation, not replacement” – using AI for repetitive tasks while retaining responsibility for core logic and final validation. A graphic design student drew a line that was philosophically precise: “Using AI to do a final work for me, I say no, but to help me make a final work as a tutor or a supporter or a friend, I will say yes.”

These are not students waiting for better policy. They have outrun it. But they are doing this intellectual and moral work largely in isolation – and often in silence. One engineering student said she hadn’t heard others discuss their AI use “as it might look like cheating or like they do not understand the assignment.” The furtiveness is itself a cost. Universities have an untapped resource in students’ own ethical reasoning – and creating structured opportunities for that reasoning to be surfaced and shared would do more than another round of policy revision.

We urgently need to create a forums in which that reasoning can shared and elaborated upon. In fact I think this needs to be at the heart of any adequate institutional strategy. If we don’t account for the complexity of these existing students culture of AI use then policy and practice will be at a remove from the real needs and interests of students.

It means that we shouldn’t conceive of critical AI literacy in terms of a deficit which we need to fill with knowledge. Instead it could be seen as designing occasions through which existing reasoning could be elicited, elaborated and systematised in a way that makes it visible to others. The institutional response has been inadequate and an adequate response needs to be grounded in taking student responses to that prior inadequacy seriously.

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