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The risks of integrating AI too rapidly into higher education

I thought this was an interesting announcement from Surrey. I certainly see the appeal of being able to publicly say you’re doing this:

Rather than introducing AI as an additional skill, Surrey is undertaking a systematic redesign of every degree programme. This transformation will ensure that, as AI becomes embedded across society and the economy, Surrey students retain deep disciplinary expertise, develop critical judgement, and understand the implications of deploying AI within their field. In a course on voting behaviour, for instance, students are given a simple brief: pick an election, ask ChatGPT to explain the result, then interrogate whether AI got it right – testing its response against established theories of political behaviour, datasets from the British Election Study and published academic research. Students are enabled to identify and explain where the AI is vague or inaccurate, so that AI is not a shortcut but a prompt for deeper analysis. 

Every student – from foundation year through to postgraduate – will develop applied, discipline-specific AI capability as a core part of their studies. Surrey graduates will be equipped to shape how AI is used, challenged and governed in real-world contexts – ensuring that they remain trusted, capable and competitive in a labour market reshaped by AI.

What does “discipline-specific AI capability” mean though? My concern is that the current commercial landscape is so profoundly uncertain (given token economics etc) that a masterplan about what capacities to equip graduates with is doomed to failure. You either specify ‘capability’ in abstraction from current tools and risk a lack of practical relevance or you specify it in terms of current tools and risk being rendered out of date when those tools go through a rapid shift in terms of access, resourcing and capabilities. It’s not tenable to do nothing. But I think doing too much, all at once, risks being counter productive by locking in approaches and techniques which risk obsolence.

There is a stated commitment that AI “will be used selectively and purposefully, only where it improves educational outcomes, while ensuring that core competencies, from clinical reasoning and legal judgement to engineering design and creative practice, are preserved and strengthened”. I agree! But this is work which needs careful learning design, it needs identification of best practice, it needs willing colleagues. It’s a long-term direction of travel, rather than an outcome. I just don’t see how you do it without having an army of learning designs working 24/7 with academic teams suddenly full of time for development.

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