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Abstract

Introduction. Growing learner diversity, expanding biomedical knowledge, and rapid clinical digitization have exposed the limits of on-size-fits-all health sciences training. Competency-Based Education (CBE) has sharpened the focus on outcomes, yet many programs still move learners through fixed sequences, infrequent assessments, and retrospective remediation. Precision Education (PE) offers a complementary, data-enabled approach that uses learning analytics, artificial intelligence (AI), and continuous feedback loops. Embedded within CBE, PE converts competencies from static milestones into dynamic trajectories that can be measured, visualized, and adjusted in real time across the curriculum. This abstract highlights the need for an interprofessional strategy to design, fund, and scale PE across medicine, nursing, rehabilitation, and allied health care professions. Without shared standards, coordinated governance, and sustained investment, small uncoordinated pilot programs risk widening disparities between well-resources and resource-limited institutions. With coordination, however, PE can personalize learning while upholding rigorous, profession-wide expectations and advancing equity.

 

Discussion. PE operationalizes a cyclical process of problem analysis, planning, learning, and data-informed adjustment, applied at the micro (learner), meso (program), and macro (institution) levels to align experiences with competency outcomes. AI and learning analytics enable early risk detection, adaptive content, richer simulation, and timely coaching, while dashboards make progress visible to learners and faculty. Trustworthy use demands transparent data governance, interpretable models, where feasible, human-in-the-loop decisions for consequential actions, and routine equity audits to detect and mitigate bias. There are several illustrative initiatives that already show feasibility. For example, national priorities that elevate PE, program exemplars that analyze clinical exposure, and EHR activity to tailor coaching and rebalance workloads, in addition to competency-based programs that advance students according to mastery all demonstrate feasible initiatives for health science education. Anticipated benefits include improved competence and confidence for learners, more targeted teaching and resource allocation for faculty and institutions, and better clinical readiness for patients and systems. Major barriers remain fragmented platforms and limited infrastructure; faculty capability gaps and change fatigue; and ethical concerns about privacy, fairness, and explainability. To move from pilots to infrastructure, we recommend a coordinated, interprofessional strategy. This includes a national consortium to set vocabulary and reference architectures; shared benchmarks focused on outcomes and equity; stackable micro-credentials in learning analytics and AI-enhanced instruction; and aligned policy and funding to equip resource-limited programs. Immediate steps include forming steering committees, mapping data sources, launching low-stakes pilots with equity metrics, and sharing competency dashboards.

 

Conclusion. PE is a timely, practical extension of CBE that personalizes learning while preserving rigorous standards. By integrating interoperable data, AI-enabled analytics, and continuous feedback loops, programs can provide precise support, improve progression equity, and strengthen clinical readiness. Establishing a consortium, codifying shared frameworks, investing in faculty capability, and tying funding to equity and readiness gains can accelerate adoption. Acting now with focused pilots, transparent dashboards, and learning communities will move the field from isolated innovations to a durable, equitable infrastructure in which every learner receives the right support at the right moment, and every patient benefits from a workforce prepared for practice.

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