Claims

Geographical region significantly moderates AI literacy intervention effects, with Europe and East Asia outperforming North America

Claim · All claims
Evidence · 1 study · quant-synthesis r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q3 i? Geographical region is a significant moderator (F(4, 167) = 2.603, p < .05); Europe (g = 0.960) and East Asia (g = 0.782) yielded larger effects than North America (g = 0.658). → Suya Liu 2026

Evidence

Suya Liu 2026

Suya Liu, Ziyan Lin, Oi Lam Ng, Yijian Yang, Yun Dai. (2026). AI literacy interventions in education: A meta-analysis of effects and moderators. Educational Research Review. https://doi.org/10.1016/j.edurev.2026.100813

q3 · i? · quant-synthesis · r2

Moderator analysis in the three-level meta-analysis testing five regions. Europe (g = 0.960), East Asia (g = 0.782), and North America (g = 0.658) showed progressively lower significant effects; the Southeast Asia estimate rests on only k = 4 effect sizes.

"Geographical region was a significant moderator (F (4, 167) = 2.603, p < .05). The highest effect was observed in Southeast Asia (g = 1.680), though this finding was based on a small number of effect sizes (k = 4)"

Discussion

Learner Variables

Page metadata
Field Value
type claim
id geographic-region-moderates-ai-literacy-effects
title Geographical region significantly moderates AI literacy intervention effects, with Europe and East Asia outperforming North America
description Geographical region significantly moderates AI literacy intervention effects, with Europe and East Asia outperforming North America
status review
evidence_strength moderate
newest_evidence 2026
generated by process:wiki-ingest on 2026-10-12
trust unverified — no one has confirmed this page's content
sources 1 — suya-liu-2026

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