Developing an AI Clinical Reasoning Tutor to Strengthen Student Engagement Before Clinical Placement

Abstract

This presentation describes the development and early use of an AI-assisted clinical reasoning tutor in a fourth-year clinical exercise physiology unit delivered immediately prior to clinical placement. The tutor was designed to support students as they transition from theoretical knowledge to applied, defensible clinical decision-making in a safe simulated environment. Rather than providing answers, the AI assistant functions as a Socratic clinical educator, requiring students to justify assessment choices, intervention decisions, progression criteria, referral considerations and scope-of-practice boundaries before advancing through each phase of rehabilitation planning.

The tutor guides students through sequential clinical reasoning stages, including initial assessment, baseline measurement, goal setting, intervention planning, monitoring and discharge planning. Its design emphasises tissue healing, evidence-informed practice, risk-benefit analysis, multidisciplinary collaboration, and Accredited Exercise Physiologist professional scope. Early student feedback indicates that the simulation improved perceived clinical reasoning and decision-making, supported learning through Socratic questioning, and strengthened understanding of scope and referral boundaries. Students particularly valued being required to justify decisions, reporting that the activity made them think more deeply about why they were asking questions or selecting assessments.

The presentation highlights how carefully scaffolded AI simulation can promote student engagement, reflective practice and placement readiness.