Using Artificial Intelligence Tools in Teaching Biophysics

Artificial intelligence tools can support explanations, formative feedback and the preparation of learning tasks in medical biophysics. Their educational value depends on whether students develop reasoning that remains available when the tool is removed. This article examines evidence relevant to the use of generative artificial intelligence and proposes a teaching approach for undergraduate medical education. A focused narrative synthesis of educational studies, methodological commentary and international guidance was combined with the design of a biophysics learning activity. The reviewed evidence indicates that carefully structured tutoring can improve learning in particular settings, whereas unrestricted assistance can improve task completion without improving independent performance. The proposed approach requires an initial student solution, a bounded dialogue with an artificial intelligence tool, verification against physical principles and a subsequent unaided explanation. An ultrasound example demonstrates how a plausible answer can contain a conceptual error that dimensional analysis alone does not detect. Assessment addresses model selection, quantitative reasoning, verification and transfer to a changed problem. The article provides a practical design for supervised use of artificial intelligence, with attention to multilingual learning, equitable access and protection of educational data. Its contribution is methodological; no original student intervention data or measured effects on achievement are reported.