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AI-Assisted L2 Assessment: A Biblio-Systematic Analysis

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Title: AI-Assisted L2 Assessment: A Biblio-Systematic Analysis
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
FullText Text:
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  Data: AI-Assisted L2 Assessment: A Biblio-Systematic Analysis
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  Data: <searchLink fieldCode="AR" term="%22Ecem+Kopuz%22">Ecem Kopuz</searchLink><br /><searchLink fieldCode="AR" term="%22Galip+Kartal%22">Galip Kartal</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22PASAA%3A+Journal+of+Language+Teaching+and+Learning+in+Thailand%22"><i>PASAA: Journal of Language Teaching and Learning in Thailand</i></searchLink>. 2025 70:340-370.
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  Label: Availability
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  Data: 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
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  Data: Y
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  Data: 31
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  Data: 2025
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  Data: Journal Articles<br />Information Analyses
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Instruction%22">Individualized Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Reports%22">Research Reports</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Citation+Analysis%22">Citation Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Scoring%22">Scoring</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Motivation%22">Learning Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Efficiency%22">Efficiency</searchLink>
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  Data: 0125-2488<br />2287-0024
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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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  Data: EJ1478189
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 31
        StartPage: 340
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Computer Software
        Type: general
      – SubjectFull: Technology Integration
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Individualized Instruction
        Type: general
      – SubjectFull: Bibliometrics
        Type: general
      – SubjectFull: Research Reports
        Type: general
      – SubjectFull: Second Language Instruction
        Type: general
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Citation Analysis
        Type: general
      – SubjectFull: Instructional Effectiveness
        Type: general
      – SubjectFull: Scoring
        Type: general
      – SubjectFull: Learning Motivation
        Type: general
      – SubjectFull: Learner Engagement
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      – SubjectFull: Efficiency
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    Titles:
      – TitleFull: AI-Assisted L2 Assessment: A Biblio-Systematic Analysis
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            NameFull: Ecem Kopuz
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            NameFull: Galip Kartal
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              Type: published
              Y: 2025
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              Value: 70
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            – TitleFull: PASAA: Journal of Language Teaching and Learning in Thailand
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