Bibliographic record
Abstract
This qualitative observational study examines the integration of AI tools (DeepSeek/ChatGPT) in tertiary EFL writing courses. Over a 6-week period, we tracked 87 Chinese learners’ experiences using classroom observations and semi-structured interviews, addressing three research questions: 1) What cognitive and behavioral challenges emerge during AI-mediated writing? 2) How does AI usage influence textual diversity? And 3) What competence gaps hinder effective AI integration in EFL writing? Findings reveal pervasive cognitive conflicts in reconciling AI outputs with original ideas (e.g., 82% reported voice appropriation concerns), textual homogenization in vocabulary and sentence structure (77% lexical overlap), and critical competence gaps in technical, evaluative, and ethical AI application (74%/68%/31% deficit rates). Notably, while AI enriched topic vocabulary (e.g., 33% lexical uniqueness gain via personalized prompts), it risked eroding learners’ voice agency. Furthermore, early technology dependency (avg. 4.33 self-initiated uses/session) shifted to critical avoidance (1.49 uses) as learners confronted AI’s limitations. We argue for pedagogically scaffolded AI training—emphasizing prompt personalization, critical evaluation, and ethical frameworks—to balance efficiency with originality. Implications for EFL writing curricula and teacher development are discussed.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".