A Systematic Review and Meta-Analysis of AI-Enabled Assessment in Language Learning: Design, Implementation, and Effectiveness
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ERIC Institute of Education Sciences
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| Main Authors: | , , , , , |
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| Format: | Recurso educativo Open Access |
| Language: | en |
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2025
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| _version_ | 1867181900133564416 |
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| author | Angxuan Chen Yuyue Zhang Jiyou Jia Min Liang Yingying Cha Cher Ping Lim |
| author_facet | Angxuan Chen Yuyue Zhang Jiyou Jia Min Liang Yingying Cha Cher Ping Lim Angxuan Chen Yuyue Zhang Jiyou Jia Min Liang Yingying Cha Cher Ping Lim |
| collection | Education Resources Information Center |
| contents | A Systematic Review and Meta-Analysis of AI-Enabled Assessment in Language Learning: Design, Implementation, and Effectiveness Angxuan Chen Yuyue Zhang Jiyou Jia Min Liang Yingying Cha Cher Ping Lim Artificial Intelligence Technology Uses in Education Computer Assisted Testing Language Tests Design Second Language Learning Native Language Curriculum Implementation Intervention Instructional Material Evaluation Outcomes of Education Instructional Program Divisions Time Factors (Learning) Student Evaluation 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. |
| format | Recurso educativo Open Access |
| id | eric_EJ1459028 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2025 |
| record_format | eric |
| spellingShingle | A Systematic Review and Meta-Analysis of AI-Enabled Assessment in Language Learning: Design, Implementation, and Effectiveness Angxuan Chen Yuyue Zhang Jiyou Jia Min Liang Yingying Cha Cher Ping Lim Artificial Intelligence Technology Uses in Education Computer Assisted Testing Language Tests Design Second Language Learning Native Language Curriculum Implementation Intervention Instructional Material Evaluation Outcomes of Education Instructional Program Divisions Time Factors (Learning) Student Evaluation A Systematic Review and Meta-Analysis of AI-Enabled Assessment in Language Learning: Design, Implementation, and Effectiveness Angxuan Chen Yuyue Zhang Jiyou Jia Min Liang Yingying Cha Cher Ping Lim Artificial Intelligence Technology Uses in Education Computer Assisted Testing Language Tests Design Second Language Learning Native Language Curriculum Implementation Intervention Instructional Material Evaluation Outcomes of Education Instructional Program Divisions Time Factors (Learning) Student Evaluation 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. |
| title | A Systematic Review and Meta-Analysis of AI-Enabled Assessment in Language Learning: Design, Implementation, and Effectiveness |
| topic | Artificial Intelligence Technology Uses in Education Computer Assisted Testing Language Tests Design Second Language Learning Native Language Curriculum Implementation Intervention Instructional Material Evaluation Outcomes of Education Instructional Program Divisions Time Factors (Learning) Student Evaluation |
| url | https://eric.ed.gov/?id=EJ1459028 |