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-2026-2813
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:27:56.293699Z
status: published
lens: g_space
sector: education
headline: Generative AI technologies and educational outcomes: a comprehensive meta-analysis comparing traditional and AI-driven approaches
dek: With the exponentially rapid development of information technologies, generative artificial intelligence (GenAI) technologies have been widely applied to various fields, including education. However, few studies have synthesized the pooled effect of GenAI on educational outcomes, which is an important research gap this study aims to address. Through the PRISMA-based meta-analysis and systematic review, this study concludes a central finding: GenAI technologies may generally outperform traditional and non-GenAI e…
gain_title: A meta-analysis of 53 studies found generative AI-assisted education produced higher academic achievement (g=0.40), higher-order thinking (g=0.72) and writing skills (g=0.76) than non-AI approaches.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 53 studies found generative AI-assisted education produced higher academic achievement (g=0.40), higher-order thinking (g=0.72) and writing skills (g=0.76) than non-AI approaches.
gain_evidence: GenAI-assisted education may cause significantly higher academic achievements (g=0.40, small-to-moderate effect; 95%CI=0.08~0.71) | higher-order thinking abilities (g=0.72; 95%CI=0.30~1.13, moderate-to-large effect) | writing skills (g=0.76; 95%CI=0.18~1.35, moderate-to-large effect)
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis following the PRISMA protocol pooled 53 studies comparing generative AI with non-AI teaching. Students taught with generative AI scored higher on academic achievement (g=0.40), higher-order thinking (g=0.72), learning motivation (g=0.81) and writing skills (g=0.76).

AI-generated feedback showed the largest pooled effect (g=1.27) against non-AI feedback. Game-assisted generative AI did not beat non-game-assisted AI (g=0.24, confidence interval from -0.40 to 0.88).

Results differed by country. Gains were significant in China (g=0.71) and Pakistan (g=0.75), but not in Korea or Turkey (g=0.01). Effects were significant at both university (g=0.70) and secondary level (g=0.80).
limitation: Effect sizes were significantly heterogeneous across studies (I²>50%), and several included studies were rated medium or low quality, so pooled estimates vary widely and have wide confidence intervals.
tag: Evidence-backed gain
key_points: The search covered Web of Science, Scopus, Springer, Wiley and ScienceDirect, plus dissertations and conference papers, and returned 1,096 records; 53 studies met the criteria. | Of the 683 full texts assessed, 630 were excluded, mostly for missing key data (267) or unreliable designs (205). | By the authors' quality scoring, 28 of the 53 studies were high quality, 19 medium and 6 low. | Pooled effects: academic achievement g=0.40, higher-order thinking g=0.72, writing g=0.76, motivation g=0.81. | AI feedback produced g=1.27 versus non-AI feedback, while game-assisted AI showed no significant difference. | In Korea the interval for the effect ran from -0.41 to 3.78, and in Turkey the effect was essentially nil (g=0.01). | University students (g=0.70) and secondary students (g=0.80) both showed significant gains.
rundown: The authors searched five databases in March 2025 for experimental, quasi-experimental or survey studies with a control group. Two independent raters screened records, with a third rater resolving disagreements. Effects were expressed as Hedges' g and pooled with a random-effects model after a Galbraith plot showed heterogeneity.

The included studies cover ChatGPT use in computer programming, physics, English writing, medical education and music. Academic achievement was measured mainly through standardized exams, adapted tests and assignments; writing was scored for cohesion, accuracy and content innovation.

Country results split sharply. The authors attribute the null results in Korea and Turkey to cultural preference for teacher-led instruction, curricular misalignment and technical limitations, whereas China and Pakistan used GenAI to extend resources to remote areas. These explanations come from the cited literature rather than being tested directly.

The authors suggest future work examine students with disabilities and personalised teaching strategies.
sources:
- peer_reviewed | Humanities and Social Sciences Communications | https://doi.org/10.1057/s41599-026-06903-y | 2026-03-12
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

85211e2c13dbd6bf941bed77607d3be9025dc689bb6e78dab74623e19e8e3c75

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000