Innovation in my role sometimes means being the person who says: let’s slow down and think about this differently. For me right now, that means generative AI in higher education.

The truth is that many educators find it hard to locate a confident, principled position on AI, not because they haven’t thought about it, but because the question is genuinely difficult. My view is that it also reveals something more interesting: what does AI tell us about how we’ve been designing learning in the first place? That reframe, from AI as a threat to manage to AI as a diagnostic for learning design, is the foundation of an intervention I’m currently developing for teaching staff and students across the university.

The work draws on recent UK-wide research into how students experience AI, sector intelligence from the higher education press, and a body of established learning theory that rarely makes it into staff development contexts. Rather than producing another guidance document, I’ve been building practical tools: a reflective diagnostic for module leaders, a library of redesigned assessment patterns, and a communication framework that treats staff conversations about AI as pedagogical dialogue, not policy compliance. The aim is a resource that time-poor educators can actually use. One that helps them make confident, research-backed choices that fit their personal teaching context.

What feels innovative to me isn’t the topic - everyone is talking about AI - it’s the commitment to doing it rigorously. I’ve looked outside the university to leading research and practice, challenged some assumptions that were quietly embedded in our existing provision, and taken the risk of saying that quick fixes won’t be enough. Genuine innovation, I think, means being willing to slow down before you speed up.