A systematic review and meta‐analysis of AI ‐enabled assessment in language learning: Design, implementation, and effectiveness
Abstract
Background: Language assessment plays a pivotal role in language education, serving as a bridge between students' understanding and educators' instructional approaches. Recently, advancements in Artificial Intelligence (AI) technologies have introduced transformative possibilities for automating and personalising language assessments. Objectives: This article aims to explore the design and implementation of AI‐enabled assessment tools in language education, filling the research gaps regarding the impact of assessment type, intervention duration, education level, and first language learner/second language learner (L1/L2) on the effectiveness of AI‐enabled assessment tools in enhancing students' language learning outcome. Methods: This study conducted a systematic review and meta‐analysis to examine 25 empirical studies from January 2012 to March 2024 from six databases (including EBSCO, ProQuest, Scopus, Web of Science, ACM Digital Library and CNKI). Results: The predominant design in AI‐driven assessment tools is the structural AI architecture. These tools are most frequently deployed in classroom settings for upper primary students within a short duration. A subsequent meta‐analysis showed a medium overall effect size (Hedges's g = 0.390, p < 0.001) for the application of AI‐enabled assessment tools in enhancing students' language learning, underscoring their significant impact on language learning outcomes. This evidence robustly supports the practical utility of these tools in educational contexts. Conclusions: The analysis of several moderator variables (i.e., assessment type, intervention duration, educational level and L1/L2 learners) and potential impacts on language learning performance indicates that AI‐enabled assessment could be more useful in language education with a proper implementation design. Future research could investigate diverse instructional designs for integrating AI‐based assessment tools in language education.
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The effect size for the impact of ChatGPT on students' learning performance is significantly larger than that of other AI assessment tools (g = 0.390).
“The results indicate that the effect size for the impact of ChatGPT on students' learning performance is significantly larger than that of other AI assessment tools (g = 0.390).”
From RETRACTED ARTICLE: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis (2025) · cites this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 Chen et al. 2025
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- Authors
- Chen, A., Zhang, Y., Jia, J., Liang, M., Cha, Y., & Lim, C. P.
- Affiliations
- Peking University (CN)
- Education University of Hong Kong (HK)
- Corresponding author
- Lim, Cher Ping
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2024-11-12
- Volume
- 41
- Issue
- 1
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W4404281807
- Cited by
- 67
- References
- 94
- Citations by year
- 2026: 50 · 2025: 17
- FWCI
- 3.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- language assessment, language learning outcomes, automated assessment, AI architecture, first language learners, second language learners, upper elementary students, intervention duration, educational attainment, assessment types
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