AI-Assisted L2 Assessment: A Biblio-Systematic Analysis
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| Title: | AI-Assisted L2 Assessment: A Biblio-Systematic Analysis |
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| Language: | English |
| Authors: | Ecem Kopuz, Galip Kartal |
| Source: | PASAA: Journal of Language Teaching and Learning in Thailand. 2025 70:340-370. |
| Availability: | Chulalongkorn University Language Institute. Prem Purachatra Building, Chulalongkom University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand. Tel: +66-2-218-6092; Fax: +66-2-218-6104; e-mail: pasaa.editor@gmail.com; Web site: https://www.culi.chula.ac.th/en/pasaa/1 |
| Peer Reviewed: | Y |
| Page Count: | 31 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Artificial Intelligence, Computer Software, Technology Integration, Feedback (Response), Individualized Instruction, Bibliometrics, Research Reports, Second Language Instruction, Second Language Learning, Evaluation Methods, Citation Analysis, Instructional Effectiveness, Scoring, Learning Motivation, Learner Engagement, Efficiency |
| ISSN: | 0125-2488 2287-0024 |
| Abstract: | The developments in artificial intelligence (AI) have significantly transformed second language (L2) learning and assessment, and the role of AI technologies in L2 assessment have been investigated in recent research. This study presents a bibliosystematic analysis of AI-assisted L2 assessment. Using both systematic analysis and bibliometric research approaches, the study analyzed 57 SSCI-indexed articles to address participants, research methods, research foci, AI technologies employed, as well as the effectiveness, advantages, and challenges of AI in L2 assessment. Furthermore, bibliometric analysis was conducted via co-occurrence and co-citations analyses using VOSviewer. Findings have indicated that AI tools, such as automated scoring systems and natural language processing technologies, are predominantly used in writing and speaking assessments. These tools offer personalized feedback, enhance learner motivation, and provide scalable solutions for large-scale evaluations. Despite the positive impact on engagement and efficiency, challenges remain, including technical limitations, data privacy concerns, and the need for more balanced datasets. The study also highlights the intellectual foundations of the field, mapping key authors and influential papers. This study contributes to the growing body of literature by offering a biblio-systematic analysis of AI's role in L2 assessment and identifying areas for future investigation. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1478189 |
| Database: | ERIC |
| Abstract: | The developments in artificial intelligence (AI) have significantly transformed second language (L2) learning and assessment, and the role of AI technologies in L2 assessment have been investigated in recent research. This study presents a bibliosystematic analysis of AI-assisted L2 assessment. Using both systematic analysis and bibliometric research approaches, the study analyzed 57 SSCI-indexed articles to address participants, research methods, research foci, AI technologies employed, as well as the effectiveness, advantages, and challenges of AI in L2 assessment. Furthermore, bibliometric analysis was conducted via co-occurrence and co-citations analyses using VOSviewer. Findings have indicated that AI tools, such as automated scoring systems and natural language processing technologies, are predominantly used in writing and speaking assessments. These tools offer personalized feedback, enhance learner motivation, and provide scalable solutions for large-scale evaluations. Despite the positive impact on engagement and efficiency, challenges remain, including technical limitations, data privacy concerns, and the need for more balanced datasets. The study also highlights the intellectual foundations of the field, mapping key authors and influential papers. This study contributes to the growing body of literature by offering a biblio-systematic analysis of AI's role in L2 assessment and identifying areas for future investigation. |
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| ISSN: | 0125-2488 2287-0024 |
