Perception of Medical Undergraduates on Artificial Intelligence in Medical Education: Qualitative Exploration
Bibliographic record
Abstract
Background: Artificial intelligence (AI) has revolutionized medical education by delivering tools that enhance and optimize learning. However, there is limited research on the medical students' perceptions regarding the effectiveness of AI as a learning tool, particularly in Sri Lanka. Objective: The study aimed to explore students' perceived barriers and limitations to using AI for learning as well as their expectations in terms of future use of AI in medical education. Methods: An exploratory qualitative study was conducted in September 2024, involving focus group discussions with medical students from two major universities in Sri Lanka. Reflexive thematic analysis was used to identify key themes and subthemes emerging from the discussions. Results: Thirty-eight medical students participated in 5 focus group discussions. The majority of the participants were Sinhalese female students. The perceived benefits included saving time and effort and collecting and summarizing information. However, concerns and limitations centered around inaccuracies of information provided and the negative impacts on critical thinking, social interactions (peer and student teacher), and long-term retention of knowledge. Students were confused about contradictory messages received from educators regarding the use of AI for teaching and learning. However, participants showed an enthusiasm for learning more about the ethical use of AI to enhance learning and indicated that basic AI knowledge should be taught in their undergraduate program. Conclusions: Participants recognized several benefits of AI-assisted learning but also expressed concerns and limitations requiring further studies for effective integration of AI into medical education. They expressed openness and enthusiasm for using AI while demonstrating confusion and reluctance due to the perspectives and stance of educators. We recommend educating both the educators and learners on the ethical use of AI, enabling a formal integration of AI tools into medical curricula.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".