AI-Assisted Writing: Exploring Academic Writing Strategies of Graduate Students across Disciplines through Activity Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the growing integration of artificial intelligence (AI) tools into academic writing, especially in second language (L2) contexts, there is a pressing need to understand how disciplinary background and AI-mediated environments shape students’ writing strategies. Grounded in Activity Theory, this study investigates the academic writing strategies of Chinese graduate students across disciplines when composing English academic papers with AI assistance. Through semi-structured interviews, the study identifies distinct disciplinary preferences, particularly, arts students emphasize logical coherence and rhetorical organization, while science students prioritize innovation, clarity, and technical accuracy. These differences reflect how disciplinary norms influence strategic behaviors in AI-supported writing contexts. The findings also reveal a blend of shared and discipline-specific strategies shaped by mediational tools, institutional rules, and community expectations. By framing AI as a mediating artifact within writing activity systems, this study highlights the complex interplay between technology, discipline, and strategy use. The results offer valuable insights for designing discipline-sensitive, AI-aware academic writing instruction in higher education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it