AI-assisted case-based learning and flipped classroom to improve clinical decision-making: a randomized controlled trial in reproductive medicine.

TL;DR

An AI-assisted case-based learning and flipped classroom model significantly improved theoretical knowledge, clinical decision-making skills, and self-directed learning in reproductive medicine residents compared to traditional lecture-based instruction in a randomized controlled trial.

Key findings

• AI-assisted case-based learning with virtual standardized patients and flipped classroom format yielded higher post-course theoretical test scores, Mini-CEX, and OSCE performance versus traditional lectures
• Participants in the AI-assisted group reported greater improvements in learning motivation, clinical reasoning ability, self-directed learning capacity, and perceived course effectiveness
• The intervention integrated pre-class interactive case work on the DoctorU platform followed by in-class discussions to develop clinical decision-making skills
• Baseline characteristics were balanced between groups, supporting the internal validity of the observed improvements in clinical competence

Why this matters at the bedside

Demonstrates an evidence-based training model that may improve clinical competency development in reproductive medicine residents, with potential applicability to other medical specialties facing similar educational challenges and workforce training constraints.