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What Are We Missing in AI-Supported L2 Writing Research? A Systematic Review from 2014 to 2024

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Title: What Are We Missing in AI-Supported L2 Writing Research? A Systematic Review from 2014 to 2024
Language: English
Authors: Wenqian Tang, Zhuo Wang (ORCID 0000-0001-9537-3376), Guangfen Yan
Source: Journal of Computer Assisted Learning. 2026 42(3).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 32
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Technological Literacy, Pedagogical Content Knowledge, Educational Trends, Writing Research, Instructional Design, Second Language Instruction, Learning Activities, Expository Writing, Literary Genres, Rhetoric, Informal Education, Educational Theories, Prompting, Feedback (Response), Learning Objectives, Affordances
DOI: 10.1002/jcal.70262
ISSN: 0266-4909
1365-2729
Abstract: Background: While existing reviews have mapped the efficacy of pre-GenAI tools or, more recently, explored GenAI research trends and user perceptions, a comprehensive synthesis focused specifically on the pedagogical strategies and instructional designs employed in empirical studies across both eras is needed. Objectives: This systematic review analyzes AI-supported L2 writing trends and examines the integration of technology, content, and pedagogy within instructional designs, guided by the TPACK framework from 2014 to 2024. Methods: 61 empirical studies were analysed. We employed a dual-coding framework, with one lens identifying general research trends and the second using the TPACK framework to examine specific instructional design elements like theoretical grounding and learning activities. Results: A dramatic surge in publications occurred post-2022. The research landscape is overwhelmingly concentrated in formal higher education settings, with a narrow instructional focus on academic writing genres. A significant "theory-practice" pattern was identified: while Sociocultural Theory was the most cited framework, the dominant instructional format was individual activity, suggesting a reinterpretation of the AI as a "social partner" or "More Knowledgeable Other." Over half of the studies lacked an explicit theoretical framework. AI is used primarily for language optimization and explanation, targeting the revision and editing stages, while assessment remains focused on the final writing product. Conclusions: The data reveal a disconnect between technological potential and instructional practice, evidenced by the research's narrow scope (limited to formal settings and generative capabilities), often atheoretical basis, and its focus on AI as a remedial tool.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1506804
Database: ERIC
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  Data: What Are We Missing in AI-Supported L2 Writing Research? A Systematic Review from 2014 to 2024
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  Data: <searchLink fieldCode="AR" term="%22Wenqian+Tang%22">Wenqian Tang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhuo+Wang%22">Zhuo Wang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9537-3376">0000-0001-9537-3376</externalLink>)<br /><searchLink fieldCode="AR" term="%22Guangfen+Yan%22">Guangfen Yan</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2026 42(3).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 32
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  Data: 2026
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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="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+Literacy%22">Technological Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Pedagogical+Content+Knowledge%22">Pedagogical Content Knowledge</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Trends%22">Educational Trends</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Research%22">Writing Research</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Activities%22">Learning Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Expository+Writing%22">Expository Writing</searchLink><br /><searchLink fieldCode="DE" term="%22Literary+Genres%22">Literary Genres</searchLink><br /><searchLink fieldCode="DE" term="%22Rhetoric%22">Rhetoric</searchLink><br /><searchLink fieldCode="DE" term="%22Informal+Education%22">Informal Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Theories%22">Educational Theories</searchLink><br /><searchLink fieldCode="DE" term="%22Prompting%22">Prompting</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Objectives%22">Learning Objectives</searchLink><br /><searchLink fieldCode="DE" term="%22Affordances%22">Affordances</searchLink>
– Name: DOI
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  Group: ID
  Data: 10.1002/jcal.70262
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: While existing reviews have mapped the efficacy of pre-GenAI tools or, more recently, explored GenAI research trends and user perceptions, a comprehensive synthesis focused specifically on the pedagogical strategies and instructional designs employed in empirical studies across both eras is needed. Objectives: This systematic review analyzes AI-supported L2 writing trends and examines the integration of technology, content, and pedagogy within instructional designs, guided by the TPACK framework from 2014 to 2024. Methods: 61 empirical studies were analysed. We employed a dual-coding framework, with one lens identifying general research trends and the second using the TPACK framework to examine specific instructional design elements like theoretical grounding and learning activities. Results: A dramatic surge in publications occurred post-2022. The research landscape is overwhelmingly concentrated in formal higher education settings, with a narrow instructional focus on academic writing genres. A significant "theory-practice" pattern was identified: while Sociocultural Theory was the most cited framework, the dominant instructional format was individual activity, suggesting a reinterpretation of the AI as a "social partner" or "More Knowledgeable Other." Over half of the studies lacked an explicit theoretical framework. AI is used primarily for language optimization and explanation, targeting the revision and editing stages, while assessment remains focused on the final writing product. Conclusions: The data reveal a disconnect between technological potential and instructional practice, evidenced by the research's narrow scope (limited to formal settings and generative capabilities), often atheoretical basis, and its focus on AI as a remedial tool.
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      – Text: English
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        PageCount: 32
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Technology Integration
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      – TitleFull: What Are We Missing in AI-Supported L2 Writing Research? A Systematic Review from 2014 to 2024
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