What Do We Become: The Transformative Nature of Technology in Medical Education
Bibliographic record
Abstract
The article is based on the ASME Gold Medal Address the author gave in Edinburgh in July 2025, and it explores the transformative nature of technology use and the lessons learned regarding what we become when we do use these tools and systems. Educational technologies are still sometimes used in ways that augment classroom and bedside learning, but they are rarely the focus of the conversation about technology in medical education. There is more investment in administrative, tracking and reporting technologies than in educational technologies. Indeed, on the surface, medical education today looks very similar to the way it looked decades ago, but what is happening underneath is quite different. Ambient technology means massive ambient surveillance but not by medical schools. Technology also supports backchannels between learners at different institutions and differentiated learning teams, which again do not seem to be issues that schools are attending to. This is all exacerbated by the rapid adoption of Generative Artificial Intelligence (GenAI) technologies. Given that the capabilities of a learner using technology are not the same as those of a learner not using technology and that education is all about altering capability states, why do medical educators not attend to tracking capability states (both actual and perceived)? When technology helps us to do certain things, it is always at a price. Medical educators need to better understand how technologies change beliefs, values, perceptions, customs and cultures that are central to training tomorrow's doctors.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".