Teaching Writing Skills Using Generative AI: The Paradox of Adoption and Resistance Among Language Educators
Bibliographic record
Abstract
The professional identity of language educators has been significantly influenced by the integration of generative AI tools in teaching writing skills and processes. These tools have, to some extent, assumed roles traditionally held by teachers. This study explores the paradox of adoption and resistance among language educators regarding the use of generative AI in writing instruction. Adopting a quantitative, descriptive-analytical approach, the study utilized a tripartite rubric (adoption, neutrality, resistance) to examine teachers’ attitudes across four AI-mediated writing stages: pre-writing, drafting/ initial writing, revising/editing, and publishing/feedback reception. In order to explore language educators' alignment with the dynamics of adoption, neutrality, and resistance, a total of 340 Arabic and English language teachers from secondary schools in Saudi Arabia participated in the study. Findings indicate that language educators demonstrated a neutral stance toward AI integration in the pre-writing and publishing/feedback reception stages, where AI serves as a supportive rather than a generative tool. This suggests that educators perceive AI as a facilitator in organizing ideas and refining final drafts without undermining their instructional role. Conversely, strong resistance emerged in the drafting/ initial writing and revising/editing stages, where AI directly engages in text production and modification. This reflects educators' concerns about diminished student engagement in writing development and the potential erosion of their professional role. Additionally, these findings reveal teachers’ concerns about how AI might alter their role in teaching writing and their doubts about students’ ability to use these tools responsibly. While adoption was present across all writing stages, it remained marginal, consistently overshadowed by neutrality or resistance. This suggests that, despite some recognition of AI’s potential, most educators remain hesitant to fully embrace it.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".