Integrating AI: Challenges and Opportunities in Teaching English Writing Skills
Bibliographic record
Abstract
Artificial intelligence (AI) technology has the potential to provide personalized instruction for students and elevate administrative tasks for teachers. AI-driven tools can improve accessibility for students learning English as a second language (L2) or foreign language helping educators consider their use in teaching. This research study explores AI-enabled technology usage, challenges, and opportunities in the Arab World. Study data is gathered from semi-structured, in-depth, face-to-face, qualitative interviews with English language instructors at the University of Ha’il, KSA and Ajman University, UAE. Theoretically supported by sociocultural theory, results provided strong insights into AI technology in teaching. Results revealed that key challenges included plagiarism and the impact on students’ skills development, highlighting concerns about academic integrity and overreliance on AI tools like ChatGPT. Despite AI's potential to improve learning experiences by offering personalized support, participants emphasize the need for balanced integration and ethical use. Effective implementation needs comprehensive training for educators and maintaining a balance between AI tools and traditional teaching approaches to preserve critical thinking and creativity while using AI’s advantages. Thus, considering the findings, this study concludes that AI should supplement traditional teaching methods, ensuring that it enhances rather than replaces the crucial human aspects of education. Finally, study recommendations and implications are discussed accordingly.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".