_{Read my post below or listen to AI me read it. Have to say, I sound very well spoken in this video. To my ears doesn’t sound much like me. For those that know me: what do you think?}

As we attempt to navigate uncharted (as well as expanding and changing) landscapes of artificial intelligence in higher education, it makes sense to reflect on our approaches and understanding. We’ve done ‘headless chicken’ mode; we’ve been in reactive mode. Maybe we can start to take control of the narratives; even if what is ahead of us is disrupting, fast-moving and fraught with tensions. Here are six perspectives from me that I believe will help us move beyond the hype and get on with the engagement that is increasingly pressing but, thus far, inconsistent at best.

1. AI means whatever people think it means

In educational circles, when we discuss AI, we’re primarily referring to generative tools like ChatGPT, DALL-E, or Copilot. While computer scientists might argue- with a ton of justification- this is a narrow definition, it’s the reality of how most educators and students understand and engage with AI. We mustn’t get bogged down in semantics; instead, we should focus on the practical implications of these tools in our teaching and learning environments whilst taking time to widen some of those definitions, especially when talking with students. Interrogating what we mean when we say ‘AI’ is a great starting point for these discussions in fact.

2. AI challenges our identities as educators

The rapid evolution of AI is forcing us to reconsider our roles as educators.  Whether you buy into the traditional framing of higher education this way or not, we’re no longer the sole gatekeepers of knowledge, dispensing wisdom from the lectern. However much we might want to advocate for notions of co-creation or discovery learning, the lecturer/ teacher as expert is a key component of many of our teacher professional identities.  Instead, we need to acknowledge that we’re all navigating this new landscape together – staff and students alike. This shift requires humility and a willingness to learn alongside our students. The alternatives: Fake it until you make it? Bury your head? Neither are viable or sustainable. Likewise, this is not something that is ‘someone else’s job’. HE is being menaced from many corners and workload is one of the many pressures- but I don’t see a beneficial path that does not necessitate engagement. If I’m right then something needs to give. Or be made less burdensome.

3. Engage, not embrace

I’m not really a hugger, tbh. My family? Yes. A cute puppy? Probably. Friends? Awkwardly at best. A disruptive tech? Of course not. While some advocate for ’embracing’ AI, I prefer the term ‘engage’. We needn’t love these technologies or accept them unquestioningly, but we do need to interact with them critically and thoughtfully. Rejection or outright banning is increasingly unsupportable, despite the many oft-cited issues. The sooner we at least entertain the possibilities that some of our assumptions about the nature of writing and what constitutes cheating and how we best judge achievement may need review the better.

4. AI-proofing is a fool’s errand

Attempts to create ‘AI-proof’ assessments or to reliably detect AI-generated content are likely to be futile. The pace of technological advancement means that any barriers we create will swiftly be overcome. Many have written on the unreliability and inherent biases of detection tools and the promotion of flawed proctoring and surveillance tools only deepens the trust divide between staff and students that is already strained to its limit.  Instead, we should focus on developing better, more authentic forms of assessment that prioritise critical thinking and application of knowledge. A lot of people have said this already, so we need to build a bank of practical, meaningful approaches, draw on the (extensive) existing scholarship and, in so doing, find ways to better share things that address some of the concerns that are not: ‘Eek, everyone do exams again!’

5. We need dedicated AI champions and leadership

To effectively integrate AI into our educational practices, we need people at all levels of our institutions who can take responsibility for guiding innovations in assessment and addressing colleagues’ questions. This requires significant time allocation and can’t be achieved through goodwill alone. Local level leadership and engagement (again with dedicated time and resource) is needed to complement central policy and guidance. This is especially true of multi-faculty institutions like my own. There’s only so much you can generalise. The problem of course is that whilst local agency is imperative, too many people do not yet have enough understanding to make fully informed decisions.  

6. Find a personal use for AI

To truly understand the potential and limitations of AI, it’s valuable to find ways to develop understanding with personal engagement – one way to do this is to incorporate it into your own workflows. Whether it’s using AI to summarise meeting or supervision notes, create thumbnails for videos, or transform lecture notes into coherent summaries, personal engagement with these tools can help demystify them and reveal practical benefits for yourself and for your students. My current focus is on how generative AI can open doors for neurodivergent students and those with disabilities or, in fact, any student marginalised by the structures and systems that are slow to change and privilege the few.