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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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Bibliographic Details
Title: What Are We Missing in AI‐Supported L2 Writing Research? A Systematic Review From 2014 to 2024.
Authors: Tang, Wenqian (AUTHOR), Wang, Zhuo (AUTHOR), Yan, Guangfen (AUTHOR)
Source: Journal of Computer Assisted Learning. Jun2026, Vol. 42 Issue 3, p1-32. 32p.
Subjects: Language & languages, Bibliographic databases, Research funding, Artificial intelligence, Health, Research evaluation, Educational technology, Teaching methods, Descriptive statistics, Systematic reviews, Experimental design, Research, Conceptual structures, Research methodology, Publishing, Priority (Philosophy), College students, Written communication
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. Practitioner Notes: What is already known about this topic? ○AI tools, particularly generative AI, are increasingly used in L2 writing instruction, but integration practices remain theoretically fragmented.○The TPACK framework provides a lens for examining how technology, content, and pedagogy intersect in instructional design.○Earlier automated writing evaluation (AWE) systems showed mixed effectiveness, often failing to support lower‐proficiency learners without adequate pedagogical scaffolding.What this paper adds? ○A systematic analysis revealing that current AI integration in L2 writing research is narrow in scope (dominated by university‐level expository writing) and shallow in pedagogical depth (lacking content‐specific tool adaptations and explicit theoretical grounding).○Evidence of a critical Technological Content Knowledge (TCK) gap: studies rarely document how AI tools are adapted to specific writing genres, proficiency levels, or rhetorical purposes.○Documentation that research contexts are heavily skewed toward formal classroom settings with advanced learners, leaving informal learning spaces and diverse proficiency levels underexplored.Implications for practice and policy ○For practitioners: Move beyond "plug‐and‐play" AI adoption. Deliberately configure AI tools for specific writing contexts—customise prompts for different genres, adjust feedback parameters for proficiency levels, and align tool functions with explicit learning objectives grounded in pedagogical theory.○For researchers: Adopt rigorous reporting standards that document not only which AI tools are used but how they are pedagogically orchestrated and content‐specifically adapted. Expand research to include diverse learner populations, informal learning contexts, and longitudinal designs that capture evolving AI literacy practices.○For institutional policy: Recognize that effective AI integration requires professional development supporting teachers' TPACK development—not just technical training but pedagogical design capabilities that strategically align AI affordances with L2 writing's disciplinary demands and students' developmental needs. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
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. Practitioner Notes: What is already known about this topic? ○AI tools, particularly generative AI, are increasingly used in L2 writing instruction, but integration practices remain theoretically fragmented.○The TPACK framework provides a lens for examining how technology, content, and pedagogy intersect in instructional design.○Earlier automated writing evaluation (AWE) systems showed mixed effectiveness, often failing to support lower‐proficiency learners without adequate pedagogical scaffolding.What this paper adds? ○A systematic analysis revealing that current AI integration in L2 writing research is narrow in scope (dominated by university‐level expository writing) and shallow in pedagogical depth (lacking content‐specific tool adaptations and explicit theoretical grounding).○Evidence of a critical Technological Content Knowledge (TCK) gap: studies rarely document how AI tools are adapted to specific writing genres, proficiency levels, or rhetorical purposes.○Documentation that research contexts are heavily skewed toward formal classroom settings with advanced learners, leaving informal learning spaces and diverse proficiency levels underexplored.Implications for practice and policy ○For practitioners: Move beyond "plug‐and‐play" AI adoption. Deliberately configure AI tools for specific writing contexts—customise prompts for different genres, adjust feedback parameters for proficiency levels, and align tool functions with explicit learning objectives grounded in pedagogical theory.○For researchers: Adopt rigorous reporting standards that document not only which AI tools are used but how they are pedagogically orchestrated and content‐specifically adapted. Expand research to include diverse learner populations, informal learning contexts, and longitudinal designs that capture evolving AI literacy practices.○For institutional policy: Recognize that effective AI integration requires professional development supporting teachers' TPACK development—not just technical training but pedagogical design capabilities that strategically align AI affordances with L2 writing's disciplinary demands and students' developmental needs. [ABSTRACT FROM AUTHOR]
ISSN:02664909
DOI:10.1002/jcal.70262