ChatGPT in EFL Learning: Technology Acceptance and Learner Autonomy Among Saudi University Students
Bibliographic record
Abstract
This study explores Saudi EFL learners’ perceptions of ChatGPT and its influence on their learner autonomy, integrating the technology acceptance model (TAM) with autonomy theory. A sequential explanatory mixed-methods design was employed with 103 Saudi EFL learners at a large public university in Saudi Arabia; qualitative interviews with five participants provided deeper insight. Quantitative data came from a questionnaire on perceived usefulness, ease of use, attitudes, behavioral intention, and autonomy (self-regulation, motivation, and independent decision-making). Results showed high levels of ChatGPT use and generally positive perceptions. Consistent with TAM, perceived usefulness and perceived ease of use were strongly linked to behavioral intention, and ease of use was linked to actual use; in turn, usage was positively associated with self-regulation, motivation, and independent decision-making. Thematic analysis showed that students valued immediacy, affective safety, and flexibility, while maintaining selective trust and ethical awareness. Overall, students viewed ChatGPT as a supportive and motivating tool that can complement (not replace) instruction and associated it with higher self-reported autonomy when used responsibly. The findings also point to affective and metacognitive factors (e.g., feelings of safety, prompting literacy) that may enrich TAM-based explanations of AI acceptance in EFL contexts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".