By: Karen E. Hauer, MD, PhD and Christy Boscardin, PhD
The Challenge of Transitions in Competency-Based Education
Transitions in health professions education present significant challenges for learners and their teachers and supervisors that interrupt the aims of competency-based education (CBE). As learners progress through their training from classroom instruction to clinical practice, across clinical rotations, or between training programs, they often encounter fragmented learning experiences not aligned with their demonstrated abilities or learning needs. This persistent discontinuity can precipitate learner gaps in knowledge, inconsistent skill application, and increased cognitive load to adapt to new settings and expectations. Contributing factors include variations in curriculum and assessment design, lack of cohesive coaching and support, and insufficient communication and data-sharing between educational institutions and clinical environments. Artificial intelligence (AI) offers promising opportunities to address these challenges in transitions within health professions education.
The Opportunity to Leverage AI to Enhance Transitions
By leveraging AI, educational programs can enhance and support learners’ experience of transitions in CBE in multiple ways. AI solutions have the potential to strengthen learner agency, empower faculty and coaches, and enable programs and institutions to engage in continuous quality improvement across transitions. To support learners, AI can accelerate personalized learning by analyzing individual learning patterns and outcomes to tailor subsequent educational experiences. This personalization helps learners navigate transitions more smoothly by aligning educational content with their specific needs across competencies. AI systems can facilitate continuous assessment through automated tools that provide immediate feedback based on synthesis of multi-modal assessment information. For example, AI tools that record and summarize feedback conversations or synthesize impressions from a written patient note provide real-time performance information. AI-driven simulations, including virtual patients and virtual reality simulations of the clinical learning environment, can create realistic scenarios for healthcare professionals,1 allowing them to practice skills in a controlled environment before applying them in real-life situations. Simulated patient interactions using AI also can augment costly standardized patients for immediate access to diagnostic clinical reasoning practice and reattempts until a learner achieves mastery to qualify them to encounter similar problems with real patients. This practice reduces the uncertainties and stresses often associated with transitioning into unfamiliar clinical contexts and helps reinforce theoretical knowledge through practical application.2
AI synthesis of Assessment Data to Support Learning
AI-generated synthesis of multi-modal assessment data and predictive analytics provides a solution to delays in meaningful assessment data that helps educators monitor learners’ competence progress throughout the educational journey.3 This insight allows educators to adjust curricula to better prepare learners for the next phase of their education, ensuring that training is relevant and aligned with competency outcome standards. AI can facilitate effective, efficient faculty coaching by providing both learners and coaches with shared data about performance, including patterns and themes from narrative feedback and clinical assessments, to inform judgments about readiness to advance. This shared understanding fosters meaningful coaching conversations and supports personalized learning plans. AI can also support faculty and coaches by identifying relevant curricular activities and institutional resources aligned with the customized learning plans.
By analyzing large datasets about learners’ demonstrated competence and performance gaps, AI can identify common gaps in knowledge and skills during transitions to inform curriculum improvement. By analyzing educational data, AI can help programs and institutions allocate resources more effectively, ensuring that learners receive the support they need during critical transition points. Creation of learner handover portfolios can address gaps and barriers about a learner across levels of training4 by efficiently gathering and summarizing information throughout a learner’s experience in a program to set the stage for the next program to seamlessly tailor learning experiences to the learner’s developmental readiness. AI-enabled handover portfolios can also highlight contextual information, including differences in case load/case mix, program variations, and learner progression over time for a more holistic picture of the learner beyond their assessment data.
By leveraging AI’s capabilities, health professions educators can create a more cohesive, personalized, and supportive experience and learning environment.5 This approach not only enhances learner preparedness but also promotes smoother transitions between stages of training with continuous and cohesive attention to competency development, ultimately producing competent and confident healthcare professionals ready to serve their patients.
Refrences:
- Hebel K, Steliga A, Lewandowska K, et al. Simulated Learning, Real Emotions: The Impact of Simulation-Based Education on Nursing Students’ Stress Levels During Objective Structured Clinical Examination: A Longitudinal Observational Cohort Study. Nurs Rep. 2025;15(8):307. doi:10.3390/nursrep15080307
- Anthamatten A, Holt JE. Integrating Artificial Intelligence Into Virtual Simulations to Develop Entrustable Professional Activities. J Nurse Pract. 2024;20(9):105192. doi:10.1016/j.nurpra.2024.105192
- Lomis K, Jeffries P, Palatta A, et al. Artificial Intelligence for Health Professions Educators. NAM Perspect. 2021:10.31478/202109a. doi:10.31478/202109a
- Caretta-Weyer HA, Park YS, Tekian A, Sebok-Syer SS. The Inconspicuous Learner Handover: An Exploratory Study of U.S. Emergency Medicine Program Directors’ Perceptions of Learner Handovers from Medical School to Residency. Teach Learn Med. 2024;36(2):134-142. doi:10.1080/10401334.2023.2178438
- Luckin R, Holmes W. Intelligence Unleashed: An Argument for AI in Education. UCL Knowledge Lab; 2016. Accessed November 29, 2025. https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf
About the Author:
Dr. Karen Hauer is Vice Dean for Education and Professor in the Department of Medicine at the University of California, San Francisco (UCSF).
Dr. Christy Boscardin is Director of Artificial Intelligence and Professor in the Department of Medicine at the University of California, San Francisco (UCSF).
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