The DOI matches its Crossref record; 94 of the 96 references it cites were found in a registry or an index.
AI in informal digital English learning: A meta-analysis of its effectiveness on proficiency, motivation, and self-regulation
Open accessAbstract
This meta-analysis examines the efficacy of generative artificial intelligence (GenAI) in second language acquisition within self-directed, out-of-classroom informal contexts. A total of 15 studies meeting the inclusion criteria were identified that examined the impact of GenAI on second-language proficiency, motivation, and self-regulation. GenAI was shown to have significant effects on English proficiency and self-regulation, demonstrating its versatility in enhancing language learning outcomes. However, GenAI failed to show significant effects on learning motivation, and based on this finding we highlight the need to develop measures of motivation that are suitable for GenAI in education. Possible ways to apply GenAI in the informal language learning environment are also discussed based on the included literature.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This finding agrees with the results of Guan et al. (2024) and may be attributed to the consistent positive feedback students receive from ChatGPT during extended usage.”
From RETRACTED ARTICLE: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis (2025) · cites this paper as Guan et al. 2024
Citations
Cites
1 retracted
Zhang, Y. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/7900493 · Cited by 26
RetractedBias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722
The Cochrane Collaboration's tool for assessing risk of bias in randomised trials
Higgins, J. P. T., Altman, D. G., Gotzsche, P. C., Juni, P., Moher, D., Oxman, A. D., Savovic, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., et al. (2011) · BMJ · doi:10.1136/bmj.d5928 · Cited by 33,817
Statistical Power Analysis for the Behavioral Sciences
Cohen, J. (2013) · doi:10.4324/9780203771587 · Cited by 25,373
Duval, S., & Tweedie, R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00455.x · Cited by 14,099
Statistical Methods for Meta-Analysis.
Freeman, P. R., Hedges, L. V., & Olkin, I. (1986) · Biometrics · doi:10.2307/2531069 · Cited by 4,840
Deci, E. L., & Ryan, R. M. (2012) · Handbook of Theories of Social Psychology: Volume 1 · doi:10.4135/9781446249215.n21 · Cited by 3,161
Outlier and influence diagnostics for meta-analysis
Viechtbauer, W., & Cheung, M. W.-L. (2010) · Research Synthesis Methods · doi:10.1002/jrsm.11 · Cited by 2,949
Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields
Kmet, L. M., Cook, L. S., & Lee, R. C. (2004) · University of Alberta Library · doi:10.7939/r37m04f16 · Cited by 1,682
Birch, J. B., & Tukey, J. W. (1978) · Journal of the American Statistical Association · doi:10.2307/2286300 · Cited by 1,465
Research works printed but not linked: 7
- Bailey, 2019, Online peer feedback tasks: Training for improved L2 writing proficiency, anxiety reduction, and language learning strategies, Call-Ej, 20, 70
- Cooper, 2015, Vol. 2
- Lai, 2022, Association between the characteristics of out-of-class technology-mediated language experience and L2 vocabulary knowledge, Language, Learning and Technology, 26, 1
- Li, 2016, The construct validity of language aptitude: A meta-analysis. Studies in Second Language, Acquisition, 38, 801
- Nagender, 2017, WhatsApp auto responder using natural language processing and AI, International Journal of Computer Engineering &Technology, 8, 15
- Wang, 2021, Factors affecting the adoption of AI-based applications in higher education, Educational Technology & Society, 24, 116
- Wu, 2023, Do AI chatbots improve students learning outcomes? Evidence from a meta‐analysis, British Journal of Educational Technology, 1
Cited by
Sorted by citations
Wang, J., & Fan, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04787-y · Cited by 322
RetractedWu, H., & Wang, Y. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102134 · Cited by 149
Teng, M. F. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102157 · Cited by 58
Xia, Q., Li, W., Yang, Y., Weng, X., & Chiu, T. K. F. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100455 · Cited by 45
The Motivational Impact of GenAI Tools in Language Learning: a Quasi‐Experiment Study
Jiang, R., Gang, Y., & Shen, Q. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.12701 · Cited by 36
Metadata
- Authors
- Guan, L., Li, S., & Gu, M. M.
- Affiliations
- Education University of Hong Kong (HK)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Guan, Lihang
- Gu, Mingyue Michelle
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 7
- Pages
- 100323
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4403915833
- Semantic Scholar
- c800cfd39878472245f2f6ca71d01b320d52c501
- Cited by
- 72
- References
- 96
- Citations by year
- 2026: 43 · 2025: 28 · 2024: 1
- FWCI
- 16.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › English Language Learning and Teaching
- Keywords
- generative AI, informal language learning, English as a second language, second language proficiency, student motivation, student self-regulation, self-directed learning, out-of-classroom learning, artificial intelligence in education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
