The Transformative Impact of AI-Powered Tools on Academic Writing: Perspectives of EFL University Students
Bibliographic record
Abstract
In today’s global context, EFL learners face the challenge of mastering a new language and academic writing, especially in higher education. The study investigates how AI transforms university-level EFL students’ academic writing skills, aiming to revolutionize their approach to written language for academic success despite language barriers. Using a mixed-methods approach, this study investigates the perspectives of fifty first-year female students at Al-Baha University, Saudi Arabia, during the 2023–2024 academic year, employing both qualitative and quantitative data analysis. Using a 5-point Likert-type questionnaire and Zoom interviews, the study clarifies EFL students’ perceptions of AI writing tools. Results from the questionnaire highlight the active usage of tools such as Grammarly and GPT-3 among students. Students favor the integration AI tools into coursework, although the level of support from instructors varies. EFL students see the positive impact on writing quality but remain unsure about confidence improvement. Interviews reveal diverse tool usage, with Grammarly and ChatGPT notably favored for their adaptability and cost-free nature. The study supports integrating AI writing tools into EFL university education, emphasizing benefits such as enhanced writing quality, time efficiency, and bolstered academic integrity. The paper highlights AI’s significant impact on EFL university students’ writing skills in today’s digitally reliant world where English holds key communication importance. It underscores AI-powered tools as valuable complements to conventional writing skills, emphasizing equitable access, guidance, and collaboration between AI and educators. The study suggests strategies for creating dynamic, tech-driven learning settings that empower EFL students in their writing tasks and academic endeavors.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".