A Survey on AI Literacy for Academic English Writing Among Chinese University Students
Bibliographic record
Abstract
As generative artificial intelligence (generative AI) has been increasingly integrated into university students’ academic English writing practices, cultivating AI literacy is crucial for responsible adoption. Despite calls for systematic cultivation, empirical research on Chinese university students’ AI literacy remains limited. This study addresses this gap by investigating 313 Chinese university from different universities in the 2023–2024 academic year. Participants’ AI literacy was assessed across five dimensions (Application, Automation, Authenticity, Accountability, Agency) through a questionnaire survey. Key findings reveal: (1) Participants demonstrate moderate overall AI literacy, with weaker performance in “Application” and “Automation” dimensions; (2) A significant difference of AI literacy is identified among English writing proficiency; (3) Majors do not significantly influence overall AI literacy, though disciplinary differences emerge in the sub-dimension “Information recognizing” under the “Accountability” dimension. These results underscore the critical need for pedagogical interventions targeting AI operational skills, particularly for linguistically disadvantaged learners. Furthermore, the study contributes an empirical foundation for developing AI literacy frameworks in academic writing instruction, helping enhance students’ competitiveness in AI-mediated academic contexts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".