Discovery in Learning How We Learn in CBME through AI and Advanced Computational Tools

Competency-based medical education (CBME) is a data-intensive learning paradigm dependent on digital education tools (DETs), capturing learning data with high levels of granularity and throughput. Taking a computational approach to CBME enables more powerful translation of data into actionable insights for learners, programs, and national policy makers. This is critical to elevating ongoing engagement and the continued CBME evolution. Through a multi-dimensional, collaborative approach, our group has developed such a model. We are advancing our knowledge of how we learn through bioinformatics computational principles and AI tools. Using a speciality-agnostic, EHR-integrable, nationally-scaled, CBME platform, our medical epistemological discovery is revealing learning patterns at the level of individual learners and operation types, extending to population-scaled trends. This practice advances the field’s awareness of how medical trainees learn through computational principles and AI tools. DETs must be interoperable to establish a digital educational information web. Our platform has demonstrated this with technologic and ontologic interoperability with the American Board of Surgery’s SCORE–the national curriculum for general surgery and its related subspecialties. The future of CBME depends on medical educators who are trained in data and decision sciences and informatics to transform learning data into educational actions. A holistic computational approach to CBME data remolds data into learning knowledge and wisdom.