Advancing Medical Education: Assessing Technology‐Enhanced Learning Through the Lens of the Canadian Medical Education Directions for Specialists (CanMEDS) Framework—A Perspective Study
Bibliographic record
Abstract
Background and Aims: The CanMEDS framework is a widely adopted competency-based model that defines essential roles for physicians to ensure high-quality patient care. Technology-enhanced learning (TEL) has emerged as a transformative approach in medical education, offering flexible, interactive, and personalized learning experiences. This study explores the integration of TEL within the CanMEDS framework assessment model to enhance competency evaluation in medical education. Methods: This perspective study reviews the evolving role of TEL in medical education and its alignment with the seven CanMEDS roles: Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar, and Professional. It examines the benefits and challenges of TEL integration, including innovative assessment methods such as virtual simulations, artificial intelligence, and mobile applications. Results: TEL provides significant advantages in accessibility, scalability, and cost-effectiveness of competency assessments. Virtual reality and AI-driven tools enable realistic clinical scenarios and personalized feedback, improving the evaluation of complex skills. TEL also supports continuous learning and collaboration through digital platforms. However, challenges include ensuring assessment validity and reliability, addressing institutional barriers, faculty training needs, ethical concerns regarding data privacy, and disparities in technology access. Conclusion: Integrating TEL into the CanMEDS framework enhances medical education by offering innovative, flexible, and effective assessment methods. To maximize benefits, institutions must address technological, ethical, and pedagogical challenges through comprehensive implementation plans, faculty development, and clear guidelines. Emerging technologies hold promise for further advancing competency-based education, ensuring future physicians are well-equipped to meet evolving healthcare demands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".