Precision Education-The Future of Medical Education | ||||
| Journal of Medical Education and Practice | ||||
| Volume 1, Issue 1, March 2024, Page 17-19 PDF (241.95 K) | ||||
| Document Type: Commentary Article | ||||
| DOI: 10.21608/JMEP.2024.344302 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Senthil Kumar Rajasekaran 
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| Vice Dean for Academic and Clinical Affairs and Professor of Medicine Department of Medical Sciences Khalifa University College of Medicine and Health Sciences, Abu Dhabi, UAE. Adjunct Faculty, Wayne State University School of Medicine, Detroit, MI. Affiliate Faculty, Harvard T.H. Chan School of Public Health, Harvard University. | ||||
| Abstract | ||||
| The rapid integration of artificial intelligence (AI) in education presents a transformative opportunity to reevaluate traditional models, particularly in the realm of physician training. Despite advancements in active learning techniques, medical education largely adheres to a standardized approach, overlooking individual variations in baseline knowledge and learning preferences. As competency-based medical education gains prominence, addressing the unique needs of individual learners becomes imperative. This commentary advocates for the implementation of Precision Education (PE) in medical schools—a paradigm shift that tailors content and assessments to individual learners. PE leverages longitudinal data and learner analytics to drive personalized interventions, enhancing educational, clinical, and system outcomes. The commentary explores the impact of PE on learning outcomes, emphasizing its role in optimizing learning paths, fostering learner engagement, and promoting efficient use of time both for the faculty and the learners. Notably, PE aligns with the principles of evidence-based medicine and requires effective use of data analytics for predictive insights and actionable interventions. The equitable application of PE is crucial in addressing disparities in medical education outcomes, necessitating careful consideration of potential biases and the co-creation of interventions with and or by a diverse set of learners. The commentary also delves into practical strategies and tools, including adaptive learning platforms, learner analytics dashboards, and integration with electronic health records. However, it acknowledges barriers such as suboptimal learner engagement and the need for faculty involvement in tailoring interventions. While presenting promising evidence, the field of precision education is still evolving, requiring further research to comprehensively understand its impact, especially in the specific context of medical education. As AI continues to advance, the integration of PE in medical education stands to benefit from refined instructional design, and continuous assessment and improvement of personalized approaches. | ||||
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