Can generative AI genuinely engage pupils in geography, or does it simply make lessons busier? Jack Downs shares his MSc research into how GenAI shaped engagement across a Year 9 tectonic hazards unit, and why the technology tends to amplify good teaching rather than replace it.
Jack Downs is a geography teacher and completed his MSc in Learning and Teaching at Linacre College, University of Oxford, supervised by Dr Lauren Hammond. He is interested in how emerging technologies can be embedded in disciplinary teaching without eroding the depth of pupils’ thinking.
Bringing GenAI into the classroom
When ChatGPT arrived in schools, the debate about it polarised quickly. Optimists argued that generative AI (GenAI) could personalise learning and adapt to pupils in real time (Holmes, 2019). Critics warned of the opposite, contending that leaning on the technology risks passive, superficial learning in which pupils let the machine do the thinking (Selwyn, 2019). Geography has often been an early adopter of new classroom technology, yet subject-specific guidance on how to use GenAI well remains thin (Lane, 2025). I wanted to move the question out of the abstract and into a real classroom.
My MSc study was a mixed-methods case study across nine one-hour Year 9 lessons on tectonic hazards at a secondary school in England. I measured engagement using a tailored version of Martin’s Motivation and Engagement Scale (MES) (Martin, 2007) before and after the unit, gathered pupil-voice surveys after every lesson, kept a teacher reflective log, and interviewed geography colleagues. Throughout, I treated engagement as multidimensional (Fredricks et al., 2004), spanning behavioural, emotional and cognitive dimensions, alongside agentic engagement, the influence pupils exert on their own learning (Reeve, 2012).
The intervention
I designed nine GenAI activities around a single principle. Pupils should have to explain, critique and revise the AI’s outputs, not simply receive them. Each activity used a bespoke ChatGPT “GPT” that I had prompt-engineered for the lesson, with the school’s GenAI policy and the Department for Education’s “Keeping Children Safe in Education” (2024) guidance loaded in so that every response met the relevant safeguarding protections.
The tasks offered structured choices, adaptive scaffolding and instant formative feedback. In one lesson, pupils generated an image from their own written description of a volcanic eruption, then interrogated how well it matched the geography. In another, they wrote an exam answer and received coaching and specific next steps from a feedback GPT, completing the whole feedback loop within the lesson. Elsewhere, they interviewed a GenAI “tsunami survivor” using questions they devised themselves and designed an earthquake disaster plan collaboratively in groups.
What the numbers showed
The MES compares eleven dimensions of motivation and engagement before and after the unit. Two adaptive dimensions rose clearly. ‘Self-belief’ increased by +0.47, meaning pupils were more confident they could complete their work and do well, and ‘task management’ rose by +0.38, suggesting they organised themselves more effectively. Instant, specific feedback appeared to be doing real work here.
The picture was not uniformly positive, though, and that is what makes it interesting. ‘Valuing’ (-0.19), ‘learning focus’ (-0.12) and ‘persistence’ (-0.39) all dipped. More strikingly, the three largest changes of all were maladaptive. ‘Anxiety’ and ‘uncertain control’ each rose by +0.54, and ‘self-sabotage’ by +0.56. The rise in anxiety clustered around examinations, and the rise in uncertain control around whether the feedback pupils received genuinely helped them improve. Confidence and organisation went up, but so did unease.
What pupils said
Pupils’ own responses were overwhelmingly linked to affective engagement. “Fun” appeared 94 times across the surveys and “interesting” 45 times. The exam-feedback activity was the most popular of all, with nineteen of twenty pupils saying it made the lesson more interesting. One wrote that “the next steps really showed me how to improve”; another that “I was determined to improve using what the AI told me”. Several described the tool in relational terms, with one saying, “I interact with the AI like a friend, and it gives me hints and it tells me when I’m wrong or not.” Where activities offered genuine choice, pupils valued the ownership, noting that it was “fun to interview a [tsunami] survivor and come up with my own questions”.
Engagement or novelty?
This is the honest heart of the study, because enjoyment is not the same as learning. Where a task demanded reasoning, the exam-feedback activity above all, pupils showed genuine metacognitive reflection and built a firmer understanding rather than just being handed an answer. Where a task did not, the gains were shallower. In the image-generation lesson, some pupils wrote the barest description they could and rushed to finish, which points to novelty rather than deep thinking. A number of pupils, given the chance, simply used GenAI to obtain answers, and lower-attaining pupils sometimes needed more scaffolding before they could access a task at all. Cognitive engagement was the hardest dimension to secure, and it depended almost entirely on how the task was designed.
What it means for teaching geography
The central finding is straightforward. GenAI does not generate engagement on its own. It amplifies well-designed pedagogy when its integration supports autonomy and feedback, and it can just as easily amplify passivity when it does not. Three practical principles follow from the study.
Turn “fun” into thinking. Where pupils enjoy a GenAI task, build in a requirement to explain and justify their choices. After generating an image of a volcanic eruption, for instance, ask them to account for the geographical processes involved and link them to a case study, shifting the work from description to reasoning.
Offer autonomy within a framework. Pupils appreciated choice but felt lost when options were wide open, which is part of what drove the rise in uncertain control. Suggested pathways of varying difficulty preserve a real sense of volition while giving pupils something to hold on to.
Humanise the feedback. Pupils valued the AI’s guidance but still looked to their teacher for validation. A brief teacher-led debrief after each feedback cycle, drawing out common misconceptions and linking them to disciplinary ideas, helps develop competence and relatedness that the machine alone does not provide.
Looking ahead
This was a single-site study with a small sample and a short timeframe, so the findings are indicative rather than definitive. Even so, they suggest that geography, with its long history of adopting new tools, is well placed to shape the use of GenAI thoughtfully rather than reactively. The prize on offer is not novelty but deeper, more autonomous engagement, and whether we reach it depends far less on the technology than on the pedagogy we build around it.
References
Downs, J. and Campbell, T. (2024) Using AI to transform adaptive teaching at A level. Teaching Geography, 49, 102–104.
Fredricks, J. A., Blumenfeld, P. C. and Paris, A. H. (2004) School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109.
Holmes, W., Bialik, M. and Fadel, C. (2019) Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign: Boston, MA, USA. https://circls.org/primers/artificial-intelligence-in-education-promises-and-implications-for-teaching-and-learning
Lane, R. (2025) Mitigating risks, embracing potential: a framework for integrating generative artificial intelligence in geographical and environmental education. International Research in Geographical and Environmental Education, 1–18. DOI:10.1080/10382046.2025.2458561
Martin, A. J. (2007) Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77, 413–440.
Reeve, J. (2012) A self-determination theory perspective on student engagement. In: Christenson, S. L., Reschly, A. L. and Wylie, C. (eds.) Handbook of Research on Student Engagement. Boston, MA: Springer.
Ryan, R. M. and Deci, E. L. (2000) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.
Selwyn, N. (2019) Should Robots Replace Teachers? AI and the Future of Education. Cambridge: Polity Press.
