In my previous post, I reflected on a session Jon and I ran with more than 60 colleagues exploring what constitutes appropriate AI use in higher education. We found more agreement than I expected, but also some revealing differences when it came to complex workflows involving such things as planning work, complex calculations and summatively assessed work. My central argument was that appropriateness depends on what students are learning, what they need to demonstrate independently and where they are in their learning. The thing is, and I am told this often, it’s all very well drawing conclusions in theory but what does that mean in practice? As we work towards wholesale curriculum evaluation and practice-based (re)design of teaching, learning & assessment practices under the PMPC (Practice Makes Professional Curriculum) umbrella, staff want clarity, students need consistency and institutions need workable guidance that is cognisant of the complexity and nuance necessary when thinking about the whole AI thing. So, building on those discussions, here is some of what I am recommending at institutional, course, module and individual lecturer levels.

I think at instituional level we need to:

• Resist producing ever longer catalogues of permitted and prohibited AI behaviours, and instead establish clear principles around learning, agency, transparency, inclusion and accountability.
• Allow those principles to be interpreted meaningfully within disciplines, recognising that appropriate use will differ according to what students are learning and why. We have gone with separate staff facing and student facing principles and we are testing that flex and complexity as we go.
• Retain clear boundaries where they are necessary, particularly in relation to academic integrity, while avoiding the assumption that one institutional definition of appropriate use will work in every context.
• Use realistic AI scenarios in staff development to explore areas of congruence and conflict, not to reveal the ‘correct’ answer, but to help colleagues articulate why they draw boundaries in different places.

I also think there has to be discussion at course/ module team level. Much of this ground has been covered before but I’m finding it useful to pull it together in one place:

• Review actual assessments and identify the intellectual or professional work that students are expected to demonstrate.
• Ask explicitly which elements of that work AI can legitimately support and which students need to be capable of doing themselves.
• Consider where AI use might increase rather than diminish authenticity because it reflects contemporary professional practice.
• Recognise progression. AI use that constitutes inappropriate outsourcing at Level 4 might represent sensible professional practice at Level 6 or postgraduate level because what we expect students to know and do has changed.
• Pay particular attention to those uses sitting in the contested middle, because disagreement about them may reveal different assumptions about the purpose of an assessment.

Individual lecturers could:

• Explain why particular uses of AI are appropriate or inappropriate rather than simply telling students what they are allowed to do.
• Connect AI guidance explicitly to learning. For example: ‘You can use AI to brainstorm possible approaches, but you need to rationale your choice of approach, be able to verbally defend it in labs and undertake the final analysis yourself because developing and demonstrating that analytical capability is the purpose of this task.’
• Help students understand that the important question is often not how much AI they have used, but what intellectual work they have handed over to it.
• Encourage students to interrogate, challenge and adapt AI outputs rather than simply accepting them.
• And, of course, model critical use and engagement

For some things there is a broad congruence in thinking but this exists more at general, sweeping principles levels. The more complex things get the less likelihood of a visible line existing. I think we need to do a lot more work to make the reasons for drawing different lines visible, discussable and educationally defensible while making sure our students are meaningfully and deeply engaging with the sort of disruption connoted by these tech.

None of this removes the need for clear boundaries, and I certainly don’t think every decision about AI use should be left to individual lecturers or students. But I do want to get away from blanket bans and decisions not discussed with peers. I think traffic light systems have had an important part to play in evolving thinking but we need to acknowledge the lack of fit in so many (and increasingly complex) circumstances.

I would love to be able to say that every student understands the rationale for a teaching approach, assessment design and core principles dictating specific, tailored recommendations in relation to most appropriate sources and tools (including AI) recommended for completing a task or engaging with a topic.  But perhaps we need to become more comfortable with the idea that consistency does not always mean uniformity. We can agree on shared educational principles while recognising that their application will look different across disciplines, levels and assessments. By making differences visible, through discussion and then justified in terms of learning we have a chance of seizing the moment to effect positive changes across curricula and assessments which (to bang one of my favourite drums) is overdue anyway.