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TRUVACE RECORD VERSION
record: TRV-2026-2810
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:27:03.892861Z
status: published
lens: trace
sector: education
headline: The Effectiveness of Artificial Intelligence in Undergraduate Health Professions Education: Systematic Review and Meta-Analysis of Randomized Controlled Trials
dek: Background: Health professions education faces increasing challenges from rising health care complexity, pedagogical shifts, and constrained curricular space, and rapidly expanding knowledge and technological advances. While artificial intelligence (AI) shows promise for transforming health professions education, evidence of its effectiveness remains unclear. Objective: This study synthesized evidence from randomized controlled trials (RCTs) on the effectiveness of AI in undergraduate health professions educatio…
gain_title: A meta-analysis of 66 randomized trials (4,911 undergraduate health students) found AI chatbot-based personalized learning aids beat standard teaching on theoretical knowledge (SMD 0.53, 12 studies, 955 participants), though certainty was very low.
problem_title: A meta-analysis of 66 randomized trials of AI in undergraduate health education found most had high risk of bias, certainty ranged from low to very low, and no trial measured real-life practice or patient outcomes.
trace_subject: AI in undergraduate health professions education
gain_reading: A meta-analysis of 66 randomized trials (4,911 undergraduate health students) found AI chatbot-based personalized learning aids beat standard teaching on theoretical knowledge (SMD 0.53, 12 studies, 955 participants), though certainty was very low.
gain_evidence: theoretical knowledge (SMD 0.53, 95% CI 0.13-0.94; 12 studies; 955 participants; I²=86%), all with very low certainty
problem_reading: A meta-analysis of 66 randomized trials of AI in undergraduate health education found most had high risk of bias, certainty ranged from low to very low, and no trial measured real-life practice or patient outcomes.
problem_evidence: Most studies had high risk of bias, mainly due to poor allocation concealment and blinding, and certainty of evidence ranged from low to very low.
quick_read: A systematic review and meta-analysis pooled 66 randomized trials with 4,911 undergraduate health professions students, published 2020 to 2026, comparing AI teaching tools with standard education. The largest evidence base was AI chatbot personalized learning aids, which improved satisfaction (SMD 0.93), confidence (SMD 0.91) and theoretical knowledge (SMD 0.53).

Every one of those findings carried very low certainty. Other AI types, including content generators, rule-based chatbots and non-chatbot adaptive learning platforms, had mostly favorable estimates but wide intervals that often included no effect, and prediction intervals frequently crossed the null.

The authors conclude the evidence is insufficient to guide educational practice and that effects are inconsistent and not reliably reproducible. They recommend AI applications be used cautiously and on a trial basis.
limitation: The authors say high risk of bias, heterogeneity, imprecision and the absence of higher-level outcomes limit conclusions, and effects were inconsistent and not reliably reproducible.
tag: Dual reading
key_points: The review searched five databases up to January 26, 2026, screening 39,783 records and including 66 trials (74 records). | Satisfaction improved with chatbot-based learning aids (SMD 0.93, 7 studies, 430 participants) and confidence (SMD 0.91, 7 studies, 609 participants). | Theoretical knowledge rose by SMD 0.53 across 12 studies and 955 participants, with heterogeneity of 86%. | Subcategory analyses yielded 48 comparisons across 7 outcome domains, and most studies had high risk of bias from poor allocation concealment and blinding. | Medicine students made up 53% of trials, nursing 22.7% and dentistry 13.6%; China (14 trials) and Turkey (11) contributed most. | LLM tools were used in 38 trials (57.6%), mainly ChatGPT, and 28 trials used non-LLM systems such as convolutional neural networks and rule-based chatbots. | All trials measured only reaction or knowledge; none assessed real-life practice or patient health outcomes.
rundown: The review included randomized, crossover and cluster-randomized trials of undergraduate students in medicine, pharmacy, nursing and allied health, excluding postgraduate training and quasi-experimental studies. Of 177 papers assessed in full text, 73 were excluded for design, population or non-AI interventions, and 30 ongoing studies were identified.

Of the 66 trials, 58 were parallel RCTs, 7 were crossover trials and 1 was cluster-randomized. Median sample size was 63 participants, ranging from 16 to 426. AI served mainly as a teaching tool in 54 trials, and 36.4% of studies lasted a single session of under 3 hours.

Controls usually received standard curriculum methods such as lectures, web tools and human-led simulation (39 trials), or the same content without AI (18 trials). Outcomes were mostly measured immediately; only five studies followed students from 1 week to 3 months.

Risk of bias was rated with the ROBUST-RCT tool and certainty with GRADE. The authors used an LLM, Claude Sonnet, for preliminary data extraction, which a human verified against the full texts.
sources:
- peer_reviewed | JMIR Medical Education | https://doi.org/10.2196/88933 | 2026-03-30
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