HEPI / Kortext Roundtable dinner on Educating tomorrow: Educating the AI Generation
On Tuesday 24 March 2026, HEPI and Kortext will host a roundtable dinner in Oxford looking at ‘Educating the AI Generation’.
At this roundtable dinner, guests will receive exclusive access to the 2026 edition of the HEPI / Kortext report on students’ use and views of generative AI in their studies.
Policymakers and senior leaders from across the HE sector will come together to explore how AI might change the future university experience, considering:
- How AI changes what it means to be an expert in teaching and learning
- How should higher education institutions support staff in developing confidence and competence in using AI for teaching
- Where the gaps are between teaching staff and students’ use and knowledge of AI
- How we can ensure staff are preparing students for an AI-focused labour market
The dinner will bring together a mix of colleagues in the higher education sector, including senior leaders and policymakers from across the region. The discussion will be chaired by Nick Hilman OBE, HEPI’s Director, under the Chatham House Rule.
Please note this dinner is invitation-only. For any queries, please contact Emma [email protected] or Carole [email protected].
Comments
Jonathan Alltimes says:
Before higher education steers the oil tanker in a different direction, it would be wise to think and select where AI is likely to reduce error rather than amplify it. Have you ever thought of feeding the output of one generative AI model into another model?
Responding to the list:-
An expert is a specialist’s specialist, that is, a specialist advises another specialist on a task whose advice is most likely to be true and more likely to be true than the non-expert. An expert could minimise the error in the trugh of their advice by using AI models as a comparative standard.
Staff should understand how AI models work.
The gap between staff and students is prior experience without AI guidance, in order for use and evaluation of AI.
Real world examples, comparing work with and without AI, which have consequences for the quality of work.
Different specialisms have different rules for evaluating work. Academic judgments are non-justiciable, that is, the judgments can not be contested by an ordinary reasonable person or a judge standing in their place, in terms of probabilistic forensic analysis: an academic judgment is not the summation of an argument about states of nature.
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