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-2026-2806
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:26:21.975802Z
status: published
lens: g_space
sector: education
headline: The contingent impact of artificial intelligence on teaching effectiveness: a meta-analytic review of boundary conditions and moderating factors
dek: The digital transformation of education is reshaping pedagogical landscapes and driving the shift toward smart teaching. This meta-analysis examines the impact of Artificial Intelligence (AI)-enabled teaching on intelligent teaching assessment, synthesizing evidence from 35 pre-test-post-test designs and 37 post-test studies. Results show that AI-driven interventions yield significant positive effects, with the post-test effect size (g_p = 0.586) exceeding the pre-test-post-test gain (g_delta = 0.136), primarily…
gain_title: A meta-analysis of 37 post-test studies found AI-assisted teaching scored 0.586 standard deviations higher than comparison teaching, while 35 pre-test/post-test studies that controlled for baseline found a smaller 0.136 gain.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 37 post-test studies found AI-assisted teaching scored 0.586 standard deviations higher than comparison teaching, while 35 pre-test/post-test studies that controlled for baseline found a smaller 0.136 gain.
gain_evidence: the post-test effect size (g_p = 0.586) exceeding the pre-test-post-test gain (g_delta = 0.136)
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis in Frontiers in Psychology pooled controlled studies of AI-enabled teaching published between 2015 and 2024. Studies using only post-test comparisons (37) gave a pooled effect of g = 0.586, while studies with pre-test and post-test data (35) gave a smaller gain of g = 0.136.

The authors attribute the gap mainly to stricter baseline controls in the pre-test/post-test studies, and warn that post-test-only results may overstate real gains. Both estimates were statistically significant (p < 0.001), but results varied widely between studies.

What drove the variation was the type of AI tool and the direction of the reported results, not education stage, intervention type, publication year or location. The authors conclude that AI's benefit depends on how it is applied and on teachers' digital skills.
limitation: The authors say the dataset is too small for reliable subgroup analysis, with many subgroups holding only one or two studies, and their funnel-plot section warns the pooled effect may overestimate AI's real impact.
tag: Evidence-backed gain
key_points: The pre-test/post-test model covered 35 studies and gave a pooled effect of 0.136 (95% CI 0.071 to 0.200), which the authors call small but significant. | The post-test-only model covered 37 studies and gave a pooled effect of 0.586 (95% CI 0.521 to 0.650). | Heterogeneity was high: I² was 82.1% in the pre-test/post-test model and 74.3% in the post-test model, so effects differed widely between studies. | AI type and results direction were the strongest moderators; education stage and intervention type did not reach significance. | The literature search covered Web of Science, Scopus and ERIC for 2015 to 2024 and started with 1,681 records. | In the quality assessment of 25 appraised studies, one was rated low bias, 16 moderate and eight high. | The tools covered included generative AI and large language models, chatbots, adaptive learning systems and extended-reality or gamified learning.
rundown: Researchers searched Web of Science, Scopus and ERIC for English-language studies from 2015 to 2024 using terms such as artificial intelligence, machine learning and teaching practice, combined with experimental-design terms. They began with 1,681 records and narrowed these to 345 after screening and removing duplicates; 169 studies with missing data and 113 without control groups were among those excluded.

The analysis ran two models using standardized mean differences. The pre-test/post-test model captures change relative to starting level and gave 0.136. The post-test-only model gave 0.586. The authors say this gap shows that looking only at final scores can overestimate AI's gains.

Moderator analysis found that AI type and results direction accounted for most of the variation between studies. Control-group sample size showed marginal significance, while publication year, region and intervention duration had no significant effect.

The review describes generative AI, LLM-based teaching assistants, chatbots, adaptive systems and augmented or virtual reality as the main approaches. Several included studies reported that heavy reliance on generative AI could weaken students' grasp of basic concepts, and the authors call for teacher training in digital skills.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1744690 | 2026-04-29
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

8332ad689f4d33522923f096dd3aca9822628d3611b44936ac49c467838d561f

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000