From Prompting to Epistemic Proactivity: Temporal Trajectories of Student-AI Interaction in Mathematics Learning

Abdelghani, Kaiser & Murayama (2026) trace how middle and high school students' interactions with AI math tutors evolve over time, identifying a trajectory from superficial prompting ('tell me the answer') to what they term 'epistemic proactivity' — the active, self-directed pursuit of conceptual understanding through AI dialogue. This developmental framework is a significant contribution to AI Literacy research, as it suggests that productive AI use is not a binary skill but a capacity that matures through scaffolded practice. The temporal analysis reveals that students who receive metacognitive prompting show faster transitions to epistemic proactivity, directly linking to Metacognition and Self Regulated Learning theories. The study has implications for STEM Education and K 12 classrooms, where AI tools are increasingly used as math tutors. It challenges the assumption that students intuitively know how to learn with AI, arguing instead that deliberate scaffolding is essential — a finding that resonates across Student Experience research in AI-mediated learning environments.

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Rania Abdelghani, Peter Kaiser, Kou Murayama (2026). From Prompting to Epistemic Proactivity: Temporal Trajectories of Student-AI Interaction in Mathematics Learning. arXiv:2606.28472. cs.CY.