Sarah Dyer is Manchester’s Associate Vice-President for Teaching Excellence and Innovation. She is a social scientist whose disciplinary research has explored work, expertise, care, and ethics. Drawing on these interests, her scholarly work has focused on courage and compassion in teaching and learning, partner-enabled learning, and human-centred design in HE. She based in the Manchester Institute of Education.

In April, Senate approved the AI in Teaching and Learning Policy. The StaffNet announcement sets out the practical detail of what colleagues need to do before September and where to find support. Here, I want to reflect on the approach we took to the policy development and what it means for us.

Across the university colleagues and students are already navigating the challenges and opportunities generative AI creates. We can see a diversity of practice and sensemaking, alongside rapidly changing functionality, all of which makes it difficult to find a firm institutional footing. There are genuine differences of opinion about the role of AI in higher education, the pace of change, and what institutions like ours should be doing. To create a policy which acknowledges these disagreements but resists paralysis required bringing together a working group who could share academic expertise,  organisational insights, and experiential knowledge to work together to advise the institution.

A policy cannot hope to ‘solve’ the challenge of AI. However, as the working group met through spring 2026, what emerged consistently students and staff needed greater clarity, and we needed a common framework and language for discussing these issues. We also agreed that any policy would have to be a ‘first go’; to be refined as we learn more through applying it and as technology’s affordances change. But we do have to start somewhere.

Throughout the development of the policy, we kept students at the forefront of our minds. They have come to university to learn, often making significant sacrifices to do so, and many are understandably concerned about their future in a world shaped by AI There are no simple answers to many of the questions AI raises. But we have a responsibility to provide clarity about how students can and cannot use these tools in assessment. That is the most immediate and important thing this policy seeks to address.

Adopting a shared language

As such, our implementation approach for this academic year is deliberately focused.

We are asking teaching colleagues to categorise their existing assessments using the four-category framework and ensure that information is communicated clearly to students on Canvas before the start of term (2.1.1).

That is the requirement. It does not involve changes to learning outcomes, programme amendments or updates to course unit specifications. In some cases, categorising an assessment may prompt wider questions about assessment design. Those are valuable conversations, but they are medium-term discussions rather than something colleagues are expected to resolve over the summer.

Making time for conversations

Categorising assessments is a matter of academic judgement. The policy centres peer dialogue in this process and invites you to consider how students can inform those categorisations, either now or in the future (2.1.1).

The policy sets out how we will understand it impacts. Your feedback will shape how the policy develops and we will continue to learn from colleagues’ and students’ experience over the coming academic year. We are asking the chairs of faculty teaching and learning committees to review impacts at least once a semester (2.3.2). Additionally,  any member of our community can submit individual feedback through our feedback form (3.0.3). In this way, we will build on the work of our community as we take our next steps.

I am grateful to everyone who contributed to the development of this policy, particularly those who engaged seriously with difficult and unresolved questions. The care, expertise and commitment colleagues brought to these discussions are among the University’s greatest strengths, and they will be just as important as we move into the next phase of this work.

A particular thanks to members of the working group – – and to Senate Academic Quality and Standards Committee: Teaching, Learning and Students

Further reading:

• You can read further reflections on the policy development: https://blogs.manchester.ac.uk/itl/2026/06/19/building-a-working-consensus-on-ai-in-teaching-and-learning/

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