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TRUVACE RECORD VERSION
record: TRV-2026-2840
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:33:21.829358Z
status: published
lens: g_space
sector: education
headline: A meta-analysis examining AI-assisted L2 learning
dek: Abstract Numerous quantitative studies have investigated how artificial intelligence (AI) impacts the development of second language (L2). While individual studies delve into the effects of AI interventions on L2 learning, a meta-analysis provides a comprehensive evaluation of AI’s effectiveness in second language acquisition (SLA). Despite the growing body of meta-analytical research in AI-assisted language learning, several potential moderators have not been thoroughly investigated in previous meta-analyses. T…
gain_title: A meta-analysis of 15 studies with 2,156 participants found AI-assisted second-language learning had a large positive effect (d = 1.167) after correcting for measurement and sampling error.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 15 studies with 2,156 participants found AI-assisted second-language learning had a large positive effect (d = 1.167) after correcting for measurement and sampling error.
gain_evidence: AI-assisted L2 learning demonstrated a positive large effect with d = 1.167
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooled 15 quantitative studies covering 2,156 participants and 53 effect sizes to test whether AI-assisted instruction improves second-language learning. After correcting for measurement and sampling error, it found a large positive effect of d = 1.167.

The gains were uneven across skills and settings. AI worked better for receptive skills than productive ones, and for vocabulary more than other language skills. It was also more effective in classrooms than outside them.

The pooled results showed no significant difference between K-12 and college learners, and the type of AI-assisted interaction did not significantly change effectiveness. The authors ran moderator analyses because true effects varied significantly from study to study.
limitation: The Q statistic showed that true effect sizes varied significantly across the included studies, so the single pooled figure of d = 1.167 does not apply uniformly to every learning context.
tag: Evidence-backed gain
key_points: The analysis pooled 15 studies, 2,156 participants and 53 effect sizes on AI-assisted second-language learning. | The corrected overall effect was large and positive, d = 1.167. | AI-assisted learning was more beneficial for receptive skills than for productive skills. | AI technologies were especially strong at building vocabulary compared with other language skills. | In-class use was more effective than out-of-class use, and IMALL outperformed ICALL; K-12 and college learners did not differ significantly.
rundown: The authors set out to fill a gap in earlier meta-analyses of AI-assisted language learning, which had not thoroughly examined several potential moderators. They pooled quantitative studies and corrected effect sizes for measurement and sampling error.

The Q statistic indicated that true effect sizes varied significantly across studies, which led to a theory-based moderator analysis. Type of AI-assisted interaction was not a significant moderator.

Differences did appear by skill and setting. Receptive skills and vocabulary benefited most, in-class contexts beat out-of-class contexts, and IMALL was more impactful than ICALL. Effects for K-12 and college learners were not significantly different.
sources:
- peer_reviewed | International Review of Applied Linguistics in Language Teaching | https://doi.org/10.1515/iral-2024-0213 | 2025-03-17
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