The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2832
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:32:37.376936Z
status: published
lens: trace
sector: health
headline: Effectiveness of generative artificial intelligence-based teaching versus traditional teaching methods in medical education: a meta-analysis of randomized controlled trials
dek: BACKGROUND: Artificial intelligence (AI) has demonstrated remarkable capabilities across diverse medical applications, potentially revolutionizing healthcare delivery systems. This systematic review and meta-analysis investigated the comparative effectiveness of generative artificial intelligence (GAI)-based teaching methodologies versus conventional pedagogical approaches on educational outcomes among medical students. METHODS: We conducted a comprehensive literature search across multiple electronic databases,…
gain_title: A meta-analysis of two randomized trials (103 medical students) found generative AI teaching produced higher practical skill scores than traditional teaching (SMD 0.63, 95% CI 0.10 to 1.16).
problem_title: A meta-analysis of 11 randomized trials with 786 medical students found no significant knowledge-score difference between generative AI teaching and traditional teaching (SMD 0.27, 95% CI -0.31 to 0.85), with low-quality evidence.
trace_subject: Generative AI teaching versus traditional teaching in medical students
gain_reading: A meta-analysis of two randomized trials (103 medical students) found generative AI teaching produced higher practical skill scores than traditional teaching (SMD 0.63, 95% CI 0.10 to 1.16).
gain_evidence: GAI-based teaching had significantly higher skill scores compared to the traditional teaching method (SMD 0.63, 95% CI 0.10 to 1.16; p = 0.02; I² = 43%; Fig. 3 )
problem_reading: A meta-analysis of 11 randomized trials with 786 medical students found no significant knowledge-score difference between generative AI teaching and traditional teaching (SMD 0.27, 95% CI -0.31 to 0.85), with low-quality evidence.
problem_evidence: there was no significant difference in knowledge scores between the groups (SMD 0.27, 95% CI −0.31 to 0.85; p  = 0.36; I² = 93%; Fig. 3 )
quick_read: A systematic review and meta-analysis pooled 11 randomized trials with 786 medical students (395 taught with generative AI, 391 with traditional methods). Knowledge scores did not differ significantly between the two approaches (standardized mean difference 0.27, 95% CI -0.31 to 0.85).

Practical skills favored AI-based teaching (SMD 0.63), but that result rests on only two studies with 103 students. Students in the AI groups also reported higher satisfaction in three studies (SMD 1.18).

The authors rated the overall evidence as low quality, citing heterogeneity, thin reporting of randomization and little blinding. Long-term effects have not been measured, so the findings apply to short interventions.
limitation: The authors say statistical power may be limited by the small number of studies and modest sample sizes, substantial heterogeneity remained, and many trials poorly documented randomization and blinding.
tag: Dual reading
key_points: The review searched PubMed, Cochrane Library, EMBASE and Web of Science for studies from January 2014 to January 2025 and included 11 trials from 1,042 screened records. | All included trials were published in 2024 or 2025; 8 (72.7%) were from Asian institutions, and ChatGPT was the main tool in 9 (81.8%). | Knowledge scores showed no significant difference overall (p = 0.36), with very high heterogeneity (I² = 93%). | Subgroup analyses showed better knowledge scores with AI in interventions longer than one week (p = 0.02) and in practice courses (p < 0.01), but little effect in theory courses. | Skill scores favored AI teaching (SMD 0.63, p = 0.02) in two studies with 52 AI-taught and 51 traditionally taught students. | Satisfaction was higher in the AI group across three studies (SMD 1.18, 95% CI 0.50 to 1.87). | The GRADE rating was low; only one study had low risk for allocation concealment and only three reported blinding methods.
rundown: The authors followed PRISMA reporting and registered the protocol with INPLASY in January 2025. Two reviewers independently screened records; 1,200 were found, 158 duplicates removed, and 32 full texts assessed before 11 trials were included. Risk of bias used the Cochrane tool and evidence quality used GRADE.

Participants were mainly undergraduates, with one study including postgraduates. Five studies ran interventions longer than one week; six covered theory courses and five covered practical courses. Practice courses were defined as those held during clinical rotations or internships.

The subgroup findings suggest AI helped more in longer and practice-oriented courses, while geography and assessment timing did not change the comparison. The authors say the short, infrequent AI use in the trials may have limited results and call for standardized protocols and long-term studies.

The authors conclude AI teaching should be integrated into medical curricula, as a complement to traditional teaching, with training for educators and students.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-025-07750-2 | 2025-08-19
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

1b3d87b50007ff715ba19ec366d03e497fd240542481b2f07ae350f4dbddafce

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000