Investigating L2 writers' critical AI literacy in AI-assisted writing: An APSE model
Bibliographic record
Abstract
While the need to foster critical AI literacy (CAIL) among L2 writers has gained increasing recognition, research offering empirically grounded models for integrating CAIL into L2 writing remains limited. To contribute to the ongoing research in AI-assisted L2 writing and CAIL, we designed the current study to understand how students used ChatGPT, a popular generative AI technology, to support their writing and to uncover their CAIL in their writing practices in two first-year writing classes in the US. Adopting a qualitative case study design, we analyzed students’ interview data, written reflections, AI logs, and screencasts of students’ interactions with AI. Findings show that students utilized AI in various ways, including topic selection and brainstorming, outlining, revising, editing, and sourcing. We propose an APSE model based on four dimensions identified in students' CAIL while using ChatGPT: (1) critical awareness of AI (A), (2) critical positionality (P), (3) critical strategies for interacting with AI (S), and (4) critical evaluation of AI affordances (E). The model highlights the distinct yet overlapping components of CAIL and addresses specific concerns that L2 writers face to leverage generative AI’s linguistic and rhetorical resources critically. Pedagogical implications include explicit instruction on CAIL, developing students’ AI feedback literacy, fostering meta-skills in communication and evaluation, and enhancing their AI-assisted self-directed learning skills.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".