In Rethinking the Integration of AI in Higher Education Teaching and Learning, Lilian Schofielda and Xue Zhou consider what AI integration into the curriculum looks like in practice at a module level. They advocate “a structured process that enables educators to systematically align AI tools with AI literacy, linking the literacy process to learning objectives, class activities and assessment methods” (pg 2). This works from the principle of constructive alignment that there should be purposive integration between ILOs, teaching activities and assessment tasks which facilitates a scaffolded and coherent learning journey: the student reaches the capabilities and knowledge defined by the ILOs, in a manner robustly assessed by the assessment tasks and developed through the teaching and learning activity which takes place on the unit. What’s lacking is “simplified, practical guidance that shows educators how to integrate AI literacy holistically in the full cycle of curriculum design, aligning AI literacy with learning outcomes, class activities, and assessment” (pg 3). This is particularly problematic because of the number of educators with relatively low technological pedagogical knowledge (TPK) which makes AI-integration more challenging.
So what is AI-integration? It’s a term I use a lot and this paper challenged me to offer a more substantive definition for teaching and learning. I would argue that AI integration is the purposive incorporation of ‘AI’ into teaching and learning, working towards constructive alignment between ILOs, teaching activities and assessment tasks. I say ‘working towards’ because I think few modules are likely to reach the threshold of full constructive alignment. Indeed the pace of change (technologically and institutionally) means this might be too high a threshold for us to work with for AI-integration for the time being, which means we might think of AI-integration in the thick sense as the destination we are working towards. There can be purposive incorporation of AI into ILOs, teaching activities and assessment tasks in a more piecemeal way but full constructive alignment between them might be an outcome to be enacted over a number of design cycles. I think we should make our peace with this, as long as the incorporation is purposive and it’s informed by sufficient AI literacy (or TPK to use this jargon) to avoid the intellectual and pedagogical risks that might otherwise come from piecemeal integration. This does strain against the idea of constructive alignment however because once you talk about incorporating an objective without an associated learning activity, or a learning activity that doesn’t meet an objective (etc), there’s obvious incoherence which enters into the learning design. But at a practical institutional level arguing that AI-integration can’t take place unless it’s fully constructively aligned is a recipe for preventing AI-integration or keeping it confined to shadow practice in the classroom that doesn’t find its way into a formal module specification. As they observe on pg 11: “constructive alignment emphasises a structured and linear alignment of learning outcomes, teaching activities, and assessment methods, potentially limiting the adaptability required by rapidly evolving AI technologies.“
It follows from this that diffusion of AI in a university doesn’t make integration happen. Integration in the sense I’m advocating here isn’t simply a matter of increasing the quantity of use which takes place in an organisation. It’s the purposeful integration of the technology into organisational activities in a manner which ensures alignment between goals, activities and measurements. In this sense I think we could actually think of something like constructive alignment when thinking of AI-integration more broadly, though I’m just talking about teaching and learning here. Indeed in teaching and learning, diffusion could actually reduce AI-integration by making purposeful integration harder. If it drops into a population with low TPK then it will tend to produce unpurposive, under-literate incorporation by staff, meeting whatever happens amongst the student population. If actual classroom practice is swamped by chaotic individualized use of AI then it constitutes a challenge which needs to be undone on the teacher and/or student side before meaningful integration can occur. It means that institutionally supported diffusion can actually intensify the problem of adapting to AI (as a fact on the ground) which needs to be under some sort of control before integrating AI (as a purposive undertaking) becomes possible. As they observe on pg 5, “educators face apprehension and resistance to AI integration due to concerns about existing academic misconduct, ethical considerations, as well as the assumption that AI subverts learning”. This is exactly what I mean when I talk in my work about the tension between adapting to AI and integrating AI.
This is the practical model they call the GenAI Curriculum Alignment Model (pg 6):
1. Define intended learning outcomes
2. Design assessments that measure student achievement of these outcomes
3. Select appropriate AI tools
4. Develop teaching activities that enable students to meet the learning outcomes
5. Continuously monitor and evaluate AI integration to refine educational practices.
While I remain sceptical that learning design models as a whole simply reflect and/or guide teaching and learning practice (as opposed to constituting the categories through which the institution attempts to organise, evaluate and guide a messier and more ad hoc reality) they are particular useful in this setting because they help us think about what conditions are necessary for each stage of this process to work. What do academics need to do this? What does success look like? What are the most likely failure modes for the activity? The stages of the model all require a level of TPK which diffusion in itself (even if it’s accompanied by technical training about how to use the tool as an individual) can’t meaningfully provide. In another paper they offer some practical categories through which to think about using AI to enable active forms of learning. I used Claude Opus to summarise this bit of their discussion:
- AI as a pre-class preparation tool for foundational knowledge — Students use AI to quickly grasp basic material before deeper work, such as summarising an unread paper in ChatGPT to extract key points ahead of a group discussion.
- AI as a personalisation engine for diverse learners — Students use AI to adapt content to their own learning style and needs, such as preparing via AI before a flipped class and then organising the output collaboratively on mind-mapping tools, with options like audio-language conversion or transcription for those facing language or accessibility barriers.
- AI as a collaborating team member to be critically evaluated — Students treat AI as a project teammate whose contributions must be checked rather than trusted, such as a group task where each person works with ChatGPT, then critiques and fact-checks its output and reflects on the process.
- AI as an individual ideation stage feeding into group work — Students use AI privately to generate and sharpen their own ideas before pooling them in a group, such as researching a problem solo with AI assistance and then collaborating with peers on the same theme to completion.
In another paper the same authors offer a framework for thinking about AI literacy in terms of learning objectives for students:
- Know and understand the basic functions of AI tools to support learning
- Applying AI knowledge, concepts and applications to support learning
- Evaluating AI-generated content enabling higher-order thinking skills development
- Comprehending the moral and ethical consequences of AI and making informed decisions regarding its use in various contexts
They suggest a range of teaching and learning activities which could be aligned with each of these ILOs. I thought it was interesting that they advocate a bottom up rather than top-down approach to AI-integration. This is how they describe it on pg 8:
By bottom -up approach, we mean module organisers are at the forefront of driving AI adoption and integration. A top-down strategy, driven by the programme director or education committee, can often be bureaucratic, imposing AI adoption on educators regardless of their readiness, which can lead to resistance, particularly at a time when there is a critical need for staff to enhance their skills to prepare students for the workforce. Further, the top-down approach can often involve a lengthy approval process, requiring more time to implement changes. On the other hand, a bottom-up approach places power with the module organisers and tutors, who champion AI in their teaching and to their peers. The strategy behind this approach is to build support from educators who already integrated or are planning to integrate AI into their teaching practices. This is particularly relevant when integrating disruptive technologies like AI into educational settings.
This fits my intuitions perfectly about the need for academics to willingly engage in this process if it’s going to be real purposeful integration. If it’s done as a triaging response to institutional demands (i.e. rushed through to satisfy the request) it’s not going to be constructively aligned in the deeper meaningful sense discussed earlier in this post. They suggest this module-based bottom up approach needs to be integrated at the programme level which highlights the weakness with the top-down/bottom-up dichotomy: what matters is how AI-integration is organised into relational communities, professional teams who deliberate about more or less shared norms, which is something that ‘top-down’ demand and ‘bottom-up’ creative freedom both tend to obviate. It needs to be willing and coherent. So as well as constructive alignment for each unit, there needs to be alignment for the programme as a whole. This also suggests that AI-integration is something which should be pursued at the programme level, reducing the pressure on each unit.
My suggestion would be that unit leads who want to AI-integrate get the support to do so, on the basis that doing it properly on a smaller scale is better than doing it badly on a larger scale. Then as that process proceeds there’s a need to map the pattern of integration to make these opportunities available to a greater pool of students, so it’s a gradual process of steering integration driven by willing professional communities rather than demanding it. The assumption would be that organisational capability increases on the ground as a result of AI-integration going well: people teach with each other, students take multiple units, learning design teams develop playbooks that work in context. The assumption would be that full AI-integration neither could nor should touch every unit (and that there’s a countervailing rationale for low tech and no tech pedagogy as AI-integration proceed) but that we work towards increasing its range and scope over time by identifying the academics who want to do it, because it fits with the professional choices they’re making about what to teach their students, why and how.
Rather than seeing AI-integration as driven by centralised diffusion (with associated mandates to take advantage of the resulting opportunities) it sees it as a process of targeted cultivation, intending to find ways to spread the seeds of that cultivation in ways which will hopefully take root elsewhere, even on a small scale. It needs to be supplemented by a process of organisational learning which respects the collegiality of teaching teams and tries to deepen it as they react to the facts of AI diffusion on the ground, rather than demanding they change everything they do to meet a centrally imposed model. Integration should have a disciplinary rationale or it shouldn’t be pursued. If it doesn’t then we are effectively substituting academically-defined ILOs for centrally imposed ones that are defined on the basis of (sometimes dubious) vocational imperatives rather than a holistic picture of disciplinary knowledge. It also undermines constructive alignment because if the ILOs don’t match teaching activities and assessment then they inevitably need to change to ensure the coherence of teaching and learning. If we dispense with disciplinary rationale then we are beginning to talk about a very different model of teaching which I don’t think is appropriate for research-intensive universities. If we abandon that principle then we need to be realistic about where it leads, because it ultimately points towards disempowering academics as teachers and degrading research-led teaching as a principle of teaching and learning. A centrally-imposed discipline-blind integration model is a threat to research-led teaching itself, just as a failure to integration is a threat to learning itself because it leaves cognitive outsourcing unaddressed.
Mark Carrigan 