Generative AI challenges much of traditional Higher Education practice, since this is often built on ideas of high student autonomy, and much of the grading system focusses on assessing the characteristics of artefacts, ascribing them value through an implicit connection to student cognitive processes and development.

Much of the debate in assessment since the release of generative AI tools derives from the fact that such tools can produce artefacts with sophisticated characteristics and structure without any link to cognitive or process engagement from the learner.

In this situation how should Higher Education evolve to coexist with generative AI tools and techniques? One thing is clear, supporting/stimulating intrinsic motivation and curiosity in learners is vital. Learning requires engagement and commitment, as well as the ability to overcome challenges and master feelings of uncertainty.

In this talk I discuss methods for addressing this challenge building on the cognitive development models of Entwistle, Säljlö and colleagues.