A systematic review and meta‐analysis of AI‐enabled assessment in language learning: Design, implementation, and effectiveness.
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...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 1; pp. 1 - 20 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183981428&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183981428 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2025 vid: 41 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183981428 180819267 183981428 183981428 10.1111/jcal.13064 183981428 ppf: 1 ppct: 19 formats: tig: atl: A systematic review and meta‐analysis of AI‐enabled assessment in language learning: Design, implementation, and effectiveness. aug: au: Chen, Angxuan Zhang, Yuyue Jia, Jiyou Liang, Min Cha, Yingying Lim, Cher Ping affil: Department of Educational Technology, Graduate School of Education, Peking University, Beijing, China sug: subj: Language Development Artificial Intelligence Utilization Language Education Language Processing Evaluation Outcomes of Education Language Tests Evaluation Human Systematic Review Meta Analysis Effect Size Descriptive Statistics Automation Students, Middle School Funding Source Writing Motor Skills ab: 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. Lay description: What is currently known about this topic: Recent AI advancements offer transformative potential for automating and personalising language assessments.The effectiveness of AI‐enabled assessment tools in enhancing students' language learning outcomes is not very clear. What this paper adds: This paper systematically analyses the design of AI‐enabled language assessment tools and their implementation in educational settings.Our review indicates AI‐enabled assessment has a medium effect size on language learning in K‐12 education.Formative‐iterative tools, short intervention duration, secondary students, and L1/L2 learner all significantly contribute to the effectiveness of AI‐enabled assessment tools. Implications for practice/or policy: The integration of AI‐enabled assessment tools in education settings may be advocated for further implementation.Proper design and implementation of AI‐enabled assessment can better benefit students' language learning. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|