AI in higher education is going to get worse before it gets better

The University of Manchester warns that AI in higher education will worsen before improving, citing emerging challenges from coding agents and wearable computing to AI model degradation. The institution calls for ongoing dialogue between staff and students to build a working consensus on AI use in teaching and learning.

This internal blog post https://blogs.manchester.ac.uk/itl/2026/06/19/building-a-working-consensus-on-ai-in-teaching-and-learning/ at Manchester is largely about our new AI in Teaching and Learning policy but I wanted to share this section which is more broadly true: AI in assessment is a classic example of a wicked problem . There are only better or worse options, involving trade offs. If we imagine that a classification system is going to resolve the problem so that we can return to the status quo ex ante, we are failing to look at the sheer scale of the technological challenges which are still emerging. From coding agents through to the rapid growth of wearable computing , from AI companions through to AI labs ‘enshittifying’ their models , the emerging challenges are even more consequential than the ones we’ve faced since the launch of OpenAI’s ChatGPT in November 2022. If we can’t solve these problems, we can at least make sure we are moving in the right direction. Initiating a deeper conversation about what AI means for teaching and learning is a necessary condition for that forward movement. Nearly two years ago The Russell Group principles called for “Engagement and dialogue between academic staff and students” in order to “establish a shared understanding of the appropriate use of generative AI tools”. It was essential that “this dialogue is regular and ongoing will be vital given the pace at which generative AI is evolving”. https://blogs.manchester.ac.uk/itl/2026/06/19/building-a-working-consensus-on-ai-in-teaching-and-learning/