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-2026-2833
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:32:45.015322Z
status: published
lens: g_space
sector: education
headline: Effectiveness of AI-assisted medical education for Chinese undergraduate medical students: a meta-analysis
dek: BACKGROUND: In recent years, artificial intelligence (AI) has been adopted as an innovative teaching method. However, no studies have comprehensively evaluated the effectiveness of AI in the context of medical education in China. This study aimed to assess the effectiveness of AI-assisted teaching methods versus traditional teaching methods regarding their impact on medical education outcomes in China. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines…
gain_title: A meta-analysis of 12 studies with 824 Chinese undergraduate medical students found AI-assisted teaching raised exam scores over traditional teaching (SMD 2.06) and made students about 5.8 times more likely to report satisfaction.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 12 studies with 824 Chinese undergraduate medical students found AI-assisted teaching raised exam scores over traditional teaching (SMD 2.06) and made students about 5.8 times more likely to report satisfaction.
gain_evidence: AI-assisted teaching significantly increased students’ examination scores (SMD = 2.06, 95% CI [1.35, 2.76] | AI-assisted teaching significantly increased student satisfaction (OR = 5.80, 95% CI [3.30, 10.18]
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis of 12 studies covering 824 undergraduate medical students in China found that AI-assisted teaching beat traditional teaching on exam scores (standardized mean difference 2.06, 95% CI 1.35–2.76). In eight studies with 599 students, satisfaction was also higher with AI (odds ratio 5.80, 95% CI 3.30–10.18).

The effect was larger in longer programmes: studies running more than 12 weeks showed an SMD of 3.27, against 1.68 for those of 12 weeks or less. AI adaptive learning systems (SMD 3.35) outperformed AI-assisted diagnosis systems (SMD 1.68) in subgroup analyses.

The studies were small, ranging from 18 to 120 students, and the pooled exam-score result showed very high heterogeneity (I² = 94%). Small studies can overstate effects, so the large pooled effect needs confirmation in bigger trials.
limitation: Only 12 studies with 824 students were pooled (individual studies had 18 to 120 students), and small studies can overstate effects; most did not report blinding or allocation concealment, and satisfaction relied on non-standardized questionnaires.
tag: Evidence-backed gain
key_points: Twelve studies published between 2019 and 2024 were included, drawn from searches of seven English and Chinese databases covering January 2015 to December 2024. | Of the 824 students, 414 were in AI-assisted groups and 410 in control groups; majors were clinical medicine and medical imaging. | Nine studies were randomized controlled trials; the design of the other three was not clearly specified. | Satisfaction data came from questionnaires in eight studies involving 599 students (301 AI, 298 control), with low heterogeneity (I² = 11%). | Long-term studies (more than 12 weeks, five studies) showed an SMD of 3.27 versus 1.68 in four short-term studies. | Egger's test found no significant publication bias (P = 0.79), and a leave-one-out analysis kept all estimates within the original confidence interval. | Tools covered included ChatGPT, AI imaging diagnosis systems, AI-assisted diagnosis systems and adaptive learning platforms.
rundown: Researchers followed PRISMA guidelines and searched PubMed, Web of Science, Embase, the Cochrane Library and three Chinese databases. Of 588 initial articles, 104 were reviewed in more detail and 12 met the criteria: Chinese undergraduate medical students, randomized or non-randomized comparisons of AI and traditional teaching, and usable quantitative data.

All 12 studies reported significant gains in exam scores for the AI groups, but results varied widely (I² = 94%). Meta-regression pointed to randomization, study duration and type of AI technology as sources of the variation, while institution type, major, curriculum and sample size did not explain it. Heterogeneity fell to 0% to 28% within subgroups split by duration and by AI type.

The authors note that most studies did not report allocation concealment or blinding of participants and assessors. Satisfaction was measured with non-standardized, institution-specific questionnaires whose reliability was rarely reported. Instructor AI proficiency was seldom described, and the authors call for larger randomized trials and standardized outcome measures.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-025-07770-y | 2025-08-27
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

0d5951da9200464c95b9fbf9fa26ffa313b1bcd767572e91ef35f1bc689f06ba

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000