Sociocultural Perspectives on Artificial Intelligence in English Language Learning: Insights from Saudi University Students
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly being integrated into educational systems worldwide, offering transformative potential for language learning. This research investigates the sociocultural attitudes of Saudi university students toward AI in English language learning, examining how factors such as age, academic specialization, and economic background influence their perceptions. Data were collected from 341 male students across various disciplines at Prince Sattam University, Saudi Arabia, using a structured questionnaire adapted from the Negative Attitude toward Artificial Intelligence Scale. The findings reveal that students generally hold positive attitudes toward AI, particularly in terms of communication (mean: 3.89) and interaction (mean: 3.70), recognizing its potential to enhance language learning. However, concerns about AI’s social influence and ethical implications were evident, as reflected in the neutral stance on the “Social Influence of AI” subscale (mean: 2.79). Significant differences in attitudes were observed based on educational specialization, with medical students showing the most positive attitudes and law students expressing greater skepticism. Younger students (18-20 years) were more receptive to AI than older students, and those from higher-income families perceived AI as more effective. These findings highlight the importance of considering sociocultural factors when integrating AI into educational settings. The study underscores the need for ethical guidelines, equitable access to technology, and culturally sensitive AI tools to ensure inclusive and effective learning experiences. By addressing these dimensions, educators and policymakers can better align technological advancements with the diverse needs of learners, paving the way for more innovative and responsive educational practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".