aceModerate evidence
Published recordEducationThe Trace
More
Applying Generative Artificial Intelligence to Task-based Language Teaching and Learning: A Systematic Review and Meta-analysis
- Truvace record
- TRV-2026-2827raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 0b12b64528cb…check it
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…
62Gain side
How the two sides scoreClose call
59Problem side
The gain
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.
The problem
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).
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
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.
Sources
1 on file- TechTrendsdoi:10.1007/s11528-025-01140-7
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Student language performance with generative AI tools compared with learning without them
Hedges' g 0.75 (95% CI 0.41 to 1.10)
2,431 people
The review’s own words
There was an overall effect size of Hedges’s g = 0.75, 95% confidence interval (CI) [0.41, 1.10] without zero, indicating a large effect size at a significant level (Sullivan & Feinn, 2012).
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2827, v1: “Applying Generative Artificial Intelligence to Task-based Language Teaching and Learning: A Systematic Review and Meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2827 (accessed on the day you copy it). sha256 0b12b64528cb8ba5…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
Check this record hasn’t been altered
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2827 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.