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TRUVACE RECORD VERSION
record: TRV-2026-2827
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:31:28.022315Z
status: published
lens: trace
sector: education
headline: Applying Generative Artificial Intelligence to Task-based Language Teaching and Learning: A Systematic Review and Meta-analysis
dek: Abstract Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructiona…
gain_title: A meta-analysis of 25 studies with 2,431 English learners found students using GenAI tools in task-based language learning performed significantly better than those without, with a pooled effect of Hedges's g = 0.75.
problem_title: In the same 25-study meta-analysis of GenAI in task-based English learning, the benefit was not significant for students without AI literacy (g = 0.24), those not allowed task planning (g = 0.28) or those assessed by GenAI (g = 0.30) or by mixed assessors (g = -0.02).
trace_subject: GenAI in task-based English language teaching
gain_reading: A meta-analysis of 25 studies with 2,431 English learners found students using GenAI tools in task-based language learning performed significantly better than those without, with a pooled effect of Hedges's g = 0.75.
gain_evidence: There was an overall effect size of Hedges’s g = 0.75, 95% confidence interval (CI) [0.41, 1.10] without zero | students who applied GenAI tools in their language learning performed significantly better than those learning without GenAI tools
problem_reading: In the same 25-study meta-analysis of GenAI in task-based English learning, the benefit was not significant for students without AI literacy (g = 0.24), those not allowed task planning (g = 0.28) or those assessed by GenAI (g = 0.30) or by mixed assessors (g = -0.02).
problem_evidence: otherwise, they could only perceive a small effect size of GenAI at a non-significant level (g = 0.24, p = 0.33) | assessed by GenAI (g = 0.30, p = 0.18) | assessed by mixed assessors (i.e., GenAI and human, g = −0.02, p = 0.96)
quick_read: A systematic review and meta-analysis pooled 25 empirical studies covering 2,431 English-as-a-second or foreign-language learners. Students who used generative AI in task-based language learning outperformed those who did not, with a pooled effect of Hedges's g = 0.75 (95% CI 0.41 to 1.10), which the authors describe as large.

The benefit was uneven. Students with AI experience or training gained much more (g = 1.04, 16 studies) than those without (g = 0.24, 9 studies, not significant). Allowing task planning (g = 0.98) and having humans assess the work (g = 0.95) also went with larger gains.

Most studies used ChatGPT, a general-purpose tool not designed for language learning. The authors suggest teachers stay involved, for example as assessors, and that language-specific GenAI tools be built. Results are drawn mostly from university students in EFL settings.
limitation: The review included only English-language, peer-reviewed journal articles from three databases, covered ESL/EFL learners rather than native speakers, and drew mostly on higher-education samples, so findings may not generalise to younger learners.
tag: Dual reading
key_points: The review searched Web of Science, Scopus and ProQuest for peer-reviewed English-language studies from January 2022 to January 2025, narrowing 4,108 screened articles to 25. | The overall effect was g = 0.75, with very high heterogeneity (I2 = 93.89), so results varied widely between studies. | Students with AI experience or training showed g = 1.04 versus 0.24 (not significant) for those without. | Task planning was linked to g = 0.98 versus 0.28 without planning. | Human assessors (teachers or researchers) went with g = 0.95; GenAI-only assessment gave g = 0.30 and mixed assessment g = -0.02, neither significant. | Twenty of the 25 studies involved higher education and 4 involved K-12; 16 studies focused on writing and 6 on speaking. | Higher education showed g = 0.81 and K-12 g = 0.38, but the difference between subgroups was not statistically significant.
rundown: The authors followed PRISMA guidelines and searched three databases for studies from January 2022 to January 2025, after ChatGPT's public release. From 4,492 initial records, 384 duplicates were removed, 101 articles reached full-text screening and 25 were included. Two coders coded the studies independently with 92% intercoder reliability.

Study quality averaged 12.92 out of 18 on the MERSQI scale. Eleven studies were randomized controlled experiments, 11 were non-randomized two-group designs and 3 were single-group pre- and post-test designs. Nearly all (24 of 25) were run at a single institution.

The funnel plot looked asymmetric, suggesting possible publication bias. A fail-safe N test found 965 additional null studies would be needed to cancel the overall effect, and removing studies one at a time did not change the result.

Most studies used ChatGPT, with others using GPT-4 agents, QuillBot, Writerly and iFlytek Spark. The authors say general-purpose tools may give inappropriate feedback that confuses weaker L2 learners, so teachers need to explain how the tools work, encourage planning and act as assessors.
sources:
- peer_reviewed | TechTrends | https://doi.org/10.1007/s11528-025-01140-7 | 2025-11-05
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