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TRUVACE RECORD VERSION
record: TRV-2026-2799
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:25:29.307902Z
status: published
lens: g_space
sector: education
headline: Explainable artificial intelligence in cognitive learning psychology: A psychometric meta-analysis
dek: The purpose of this study is to examine explainable artificial intelligence (XAI) in cognitive psychology within higher education institutions. Data were retrieved from Scopus, Web of Science, and PsycINFO (1 January 2015–30 October 2025) in accordance with the PRISMA 2020 guidelines. Our initial search identified 3426 records. Of these, 1936 papers were excluded, and 616 studies underwent full-text review. In the final stage, 188 studies met the selection criteria and included 62,000 participants from higher ed…
gain_title: A meta-analysis of 188 studies with 62,000 higher-education participants found a positive pooled correlation (r about 0.32) between explainable AI in adaptive learning systems and cognitive measures such as cognitive load, attention and metacognition.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 188 studies with 62,000 higher-education participants found a positive pooled correlation (r about 0.32) between explainable AI in adaptive learning systems and cognitive measures such as cognitive load, attention and metacognition.
gain_evidence: 188 studies met the selection criteria and included 62,000 participants from higher education institutions worldwide | a global hypermean ( μ 0 ) centered on a positive association ( r ≈ 0.32 )
problem_reading: (none)
problem_evidence: (none)
quick_read: A psychometric meta-analysis in Acta Psychologica pooled 188 studies covering 62,000 higher-education participants worldwide. It looked at explainable AI inside adaptive learning systems and found a positive pooled association with cognitive outcomes, with a correlation of about 0.32.

The authors searched Scopus, Web of Science and PsycINFO for work from 1 January 2015 to 30 October 2025. They started with 3,426 records and sent 616 studies to full-text review. The findings fall into three clusters: cognitive load and neural efficiency, attention and working memory, and metacognition and affective interaction.

Pooled correlations ranged from 0.27 to 0.36, with moderate heterogeneity (I² about 58%). A correlation of this size is modest and describes an association, so it does not show that explainable AI causes the learning differences.
limitation: The pooled effect is a modest correlation (r about 0.32) with moderate heterogeneity (I² about 58%) across studies, so the association varies between studies and does not establish causation.
tag: Evidence-backed gain
key_points: The review followed PRISMA 2020 guidelines and searched Scopus, Web of Science and PsycINFO for studies from 2015 to October 2025. | The search found 3,426 records; 1,936 papers were excluded and 616 studies underwent full-text review. | 188 studies with 62,000 higher-education participants met the selection criteria. | Pooled correlations across three clusters ranged from r = 0.27 to 0.36. | The Bayesian estimate was μ = 0.33, about r = 0.32, with a 95% credible interval of 0.27 to 0.38. | Heterogeneity was moderate (I² about 58%), and model fit was reported as satisfactory (CFI = 0.96, RMSEA = 0.042). | The cognitive load and neural efficiency cluster showed the strongest consistency (C = 0.82); metacognition and affective interaction showed the weakest (C = 0.42).
rundown: The authors examined explainable AI in cognitive psychology within higher education, focusing on adaptive learning systems. They searched three databases covering 1 January 2015 to 30 October 2025 and followed PRISMA 2020 reporting guidelines.

From 3,426 initial records, 1,936 papers were excluded and 616 studies went to full-text review. The final set of 188 studies covered 62,000 participants from higher education institutions around the world.

A science mapping analysis found three clusters: cognitive load and neural efficiency (C = 0.82, D = 0.76), attention and working memory (C = 0.64, D = 0.58), and metacognition and affective interaction (C = 0.42, D = 0.39). Pooled correlations in these clusters were positive, from r = 0.27 to 0.36.

The Bayesian psychometric model put the overall effect at μ = 0.33, about r = 0.32 (95% CrI 0.27 to 0.38). Heterogeneity was moderate at I² of about 58%, and the authors concluded that explainable AI showed validity within cognitive psychology.
sources:
- peer_reviewed | Acta Psychologica | https://doi.org/10.1016/j.actpsy.2026.106899 | 2026-04-24
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