AI provides opportunities to improve assessment and feedback for educators and learners, aligning with competency-based medical education (CBME), where the focus is shifting from one-size-fits-all knowledge assessment toward personalized evaluations of measurable clinical performance. High-quality question design across cognitive levels is essential, ensuring students are evaluated not only on recall but also on application, analysis, and reasoning. Meanwhile, AI technologies such as intelligent tutoring systems and conversational bots provide scalable tools to simulate scenarios, deliver adaptive feedback, and promote reflective learning.

This session introduces participants to two complementary skill sets: (1) creating robust assessment items that measure competence across Bloom’s taxonomy, and (2) building AI-powered clinical scenario bots that guide learners through adaptive decision-making pathways. The session combines facilitator demonstrations, question design, small-group bot development, and whole-group discussion of educational value, clinical relevance, and responsible AI use.

AI-driven question generation and feedback foster personalized, self-directed learning, enabling students to arrive better prepared and reflective, for meaningful interactions with educators. Simultaneously, educators gain insights into learners’ cognitive levels and difficulties, enhancing outcomes while reducing workload and allowing greater focus on mentorship.

By the end of the session, participants will be able to integrate AI tools to design meaningful assessments, support personalized learning, and advance CBME while streamlining faculty effort.

Learning Outcomes

• Create valid and pedagogically aligned assessment items suitable for different learning contexts
• Gain increased confidence in using AI-driven platforms in both individual and collaborative settings
• Use AI to develop instructional materials, evaluate student knowledge, and provide personalized feedback
• Modify their pedagogical approach to integrate emerging technologies that enhance clinical reasoning and self-directed learning