Exploring AI-Generated Texts vs. Human-Written Texts in EFL Academic Writing: A Case Study of Qassim University in Saudi Arabia
Bibliographic record
Abstract
This research article examines the syntactic and stylistic differences between AI-generated and human-written academic articles. It also explores the success rate of plagiarism detection tools in identifying AI-generated writing in English as a Foreign Language (EFL), and faculty members' ability to distinguish between the two types. Ultimately, it examines the ethical and institutional implications of utilizing AI in academic settings. SPSS analyzed the responses received from the questionnaire. This study consisted of 52 participants: 14 EFL graduates and 38 undergraduates, 6 of whom were female. Participants were 18–24 years old, including graduates and undergraduates pursuing a bachelor's degree in English language and translation. The results showed that Qassim University graduates and undergraduates have equal familiarity with AI and its usage, scoring an average of 40.77 (graduates) and 40.48 (undergraduates). Respondents indicated the main uses of AI tools were for brainstorming, grammar checking, paraphrasing, and coherence improvement; hence ChatGPT is the most popular tool among them. All tools overall improved the writing process of students. The researchers recommend institutionalizing the teaching of AI literacy in curricula to teach students about the ethical, practical, and strategic use of such tools as ChatGPT, Grammarly, and QuillBot in writing curricula. They recommend support to provide this support through professional development for instructors to assist them in evaluating student work assisted by AI. Clear academic integrity guidelines should be created and communicated to both professors and students. Equally important, motivating students to use AI for peer evaluation, ideation, and collaborative writing, while also teaching them about the hazards of overreliance on AI and emphasizing the importance of originality and critical thinking. Finally, further research is needed to examine the long-term effects of AI. Such studies investigate the influence of AI on writing skills and academic success which should drive future policies as well as sustain excellence in writing while also ensuring the equitable and pedagogically sound integration of AI into education.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".