Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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MedEdPORTAL : the journal of teaching and learning resources
Artificial Intelligence and Large Language Models: A Case-Based, Peer-Teaching Workshop for Preclinical Medical Students
INTRODUCTION: Artificial intelligence tools have rapidly become integrated in health care settings and are quickly affecting medical student education. However, there remains limited formal teaching on such tools in student curricula. This project sought to introduce preclerkship medical students to the basics of large language models and allow them to practice ways to best use these tools to supplement their learning. METHODS: The authors designed, implemented, and evaluated a 60-minute lecture and 100-minute workshop for second-year medical students. The workshop included interactive cases covering various aspects of artificial intelligence use, and some groups were led entirely by student leaders, allowing for peer teaching. RESULTS: One hundred sixty-eight students from Harvard Medical School and Harvard School of Dental Medicine were enrolled in this session. Anonymous pre- and postsession surveys (N = 124 and N = 62, respectively) were collected and compared via unpaired t test assuming unequal variance and showed statistically significant increase in the mean ratings of six 5-point Likert scale questions assessing artificial intelligence-related knowledge/self-efficacy (P < .01) and mixed changes to 5 questions relating to attitudes/behavioral intent. DISCUSSION: Our artificial intelligence and large language model session provides a framework for teaching medical students, early in their educational journey, the basics of these tools. Our session provides interactive exercises to illustrate how best to leverage such tools while also discussing their potential risks. Such education will be important to incorporate into medical student curricula as artificial intelligence technologies grow increasingly common.
23 July 2026
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Consensus and learning climate in temporary versus permanent teams in team-based learning
BACKGROUND: In team-based learning, students are typically placed in fixed teams based on the idea that stable group membership fosters collaboration: as teammates get to know each other, they share more information, resolve differences and feel motivated to contribute. This rationale had not been tested in a randomized controlled trial. OBJECTIVE: The study compares team-based learning in temporary teams with the default permanent teams regarding individual and team readiness assurance test performance (iRAT, tRAT), reaching team consensus, the learning climate, and intrinsic motivation. DESIGN: In a randomized controlled trial, first-year medical students were assigned either to permanent TBL teams or to teams that were re-assigned for each problem. The team readiness assurance test (tRAT) votes, submitted individually and covertly, served as an indirect indicator for team consensus (concordant versus discordant tRAT vote). RESULTS: Discordant tRATs (n = 268, 11.8% of all votes) were submitted more frequently in temporary than in permanent teams, both for correct and incorrect majority decisions. The self-rated learning climate was more cooperative in permanent than in temporary teams, while intrinsic motivation and tRAT scores were similar for both types of teams. A poorer learning climate was associated with a higher proportion of discordant tRATs. CONCLUSION: Working in temporary teams does not lead to inferior intrinsic motivation; this was previously also shown for knowledge gain. However, the poorer learning climate, together with reaching a consensus less often, might indicate that at least some members of temporary teams feel not adequately appreciated in the discussion and do not accept the majority decision. With instructional strategies promoting a cooperative learning climate in temporary teams, preclinical TBL courses might serve as an early promoter of the relational team competencies required for subsequent clinical workplace learning in temporary teams.
1 July 2026
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