Conceptualizing Writing in the Digital Age: A Systematic Review of Research on AI-mediated EFL Analytical Writing
Bibliographic record
Abstract
This systematic review examines how writing is conceptualized in studies on AI-mediated English as a Foreign Language (EFL) analytical writing and how these conceptualizations are enacted in pedagogical practice. Drawing on Ivanič’s (2004) Discourses of Writing and Engeström’s (1987) Activity Theory, 20 peer-reviewed studies published between 2021 and 2024 across four main academic databases are systematically analyzed. The review examines dominant writing discourses, patterns of AI use across pre-, during, and post-writing stages, and the components of the activity system that mediate these practices. Findings show both stability and change in AI-mediated writing research. Skills and process discourses remain predominant, with 8 studies each. These studies frame writing as linguistic accuracy and procedural mastery, supported by automated evaluation tools. Meanwhile, discourses of creativity, social practices, and sociopolitical perspectives are emerging, which suggests a shift towards dialogic, collaborative, and ethically aware conceptions of writing. AI use was most common in the post-writing stage, with 18 studies, in which tools serve either as revision assistants or automated assessors. These patterns reveal tensions between accuracy and agency, and efficiency and authorship. From an Activity Theory perspective, AI acts as a dual mediating artifact that stabilizes traditional instructional routines while reconfiguring feedback structures, classroom roles, and learner agency. The review concludes that AI-mediated writing should not be seen solely as a technological enhancement but as a site for cultivating critical awareness, reflective engagement, and ethical authorship in EFL writing pedagogy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".