Generative AI as a cognitive partner: personalised GPTs and differentiated learning in undergraduate teacher education

Abstract Generative artificial intelligence is increasingly framed as a catalyst for deeper learning rather than simple automation. This design-based study investigated how personalised GPT agents were used as cognitive partners by pre-service teachers in an undergraduate Initial Teacher Education course in Digital Technologies within a Bachelor of Education (Secondary) programme. Drawing on distributed cognition, constructivism, cognitive load theory, and critical AI literacy, the intervention embedded personalised GPT agents within a university Canvas Learning Management System. Fourteen students configured and used agents aligned to curriculum goals, teaching contexts, pedagogical preferences, and weekly Prompt–Reflect–Refine tasks. Data included approximately 7,000 AI interactions, reflective journals, assessed lesson and assessment artefacts, and tutorial-group discussions. A hybrid inductive–deductive thematic analysis identified four recurrent functions in students’ use of the agents: conceptual tutor, co-designer, reflective mirror, and cognitive challenger. Across data sources, students reported clearer conceptual support, iterative planning, and reflective prompting, alongside risks of over-reliance, factual drift, and culturally narrow exemplars. The study contributes a context-specific model for LMS-integrated generative AI that foregrounds partnership, personalisation, judgement, and ethical reflexivity. Findings should be interpreted cautiously because the study was small-scale, single-site, and partly reliant on self-reported reflection.