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-2026-2794
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:24:53.585268Z
status: published
lens: g_space
sector: health
headline: The impact of integrating generative artificial intelligence into medical education on short-term learning outcomes: a systematic review and meta-analysis of randomized controlled trials
dek: BACKGROUND: Generative artificial intelligence (GenAI) technologies —exemplified by large language models (LLMs) such as ChatGPT and DeepSeek—have attracted substantial interest in medical education for their capacity to generate content, adapt to individual learners, and support interactive dialogue. Whether these technologies produce measurable improvements in educational outcomes remains uncertain, and this evidence gap has constrained broader adoption. We conducted this systematic review and meta-analysis of…
gain_title: A meta-analysis of 20 randomized trials (1,413 medical students, interns and residents) found clinical skill scores were higher with generative AI-assisted teaching than with traditional or non-AI digital teaching (SMD 1.09, moderate certainty).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 20 randomized trials (1,413 medical students, interns and residents) found clinical skill scores were higher with generative AI-assisted teaching than with traditional or non-AI digital teaching (SMD 1.09, moderate certainty).
gain_evidence: clinical skill scores (SMD = 1.09, 95% CI: 0.81–1.38, P < 0.001; k = 8; I2 = 41.0%; GRADE: moderate certainty) | We identified 20 eligible RCTs (1413 participants in total)
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis pooled 20 randomized controlled trials with 1,413 medical students, interns and residents. Trainees taught with generative AI scored higher on knowledge after teaching (SMD 0.99, low certainty) and on clinical skills (SMD 1.09, moderate certainty) than those taught traditionally or with non-AI digital methods.

Knowledge gains persisted at follow-up in four trials (SMD 0.51, moderate certainty). Publication bias was not detected, and trim-and-fill adjustment gave a similar estimate (SMD 0.90). Effects did not differ significantly by AI tool type, learner level, interaction modality or risk of bias.

The authors say the evidence is not definitive. The prediction interval for knowledge ran from -0.57 to 2.56, so a benefit in future settings cannot be assumed. They suggest generative AI may supplement conventional teaching but should not replace it until well-powered, pre-registered, multi-centre trials exist.
limitation: Certainty is low to moderate owing to high heterogeneity, risk of bias and lack of blinding, and only four studies assessed follow-up, which limits confidence in how long gains last.
tag: Evidence-backed gain
key_points: The review covered 20 randomized trials with 1,413 participants, searched across six databases from January 2020 to February 2026. | Post-intervention knowledge scores were higher in AI groups (SMD 0.99, 95% CI 0.49–1.49; 11 trials; low certainty). | Clinical skill scores were higher in AI groups (SMD 1.09, 95% CI 0.81–1.38; 8 trials; moderate certainty). | Knowledge retention at follow-up stayed significant (SMD 0.51, 95% CI 0.24–0.78; 4 trials; moderate certainty). | The knowledge prediction interval crossed zero (-0.57 to 2.56), so benefit in future settings is not guaranteed. | Knowledge results were highly heterogeneous (I2 = 86.7%), and trials lacked blinding.
rundown: The authors followed PRISMA 2020 and registered the protocol on PROSPERO. They searched PubMed, Cochrane Library, Embase, Web of Science, CNKI and Wanfang. Included trials compared generative AI-integrated teaching with traditional teaching or non-AI digital teaching among medical students, interns or residents.

Risk of bias was assessed with Cochrane RoB 2 and certainty of evidence with GRADE. Pooling used random-effects models with REML estimation. Egger's test found no publication bias (P = 0.104).

Subgroup comparisons by AI tool type, learner level, interaction modality and risk of bias were all non-significant. The authors conclude evidence certainty is low to moderate because of heterogeneity, risk of bias and absence of blinding.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-026-09320-6 | 2026-04-30
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

721fc1cbdf0131941a4f96badcdccf731ed9858ea9a40155a70abca648753f2f

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000