Abstract

Background: Generative artificial intelligence is rapidly becoming ubiquitous in higher education. However, its legitimacy within academia depends on task fit in a specific discipline, students’ knowledge of the technology, and responsible governance practices. This study explored the prevalence of artificial intelligence use, students’ perceptions of its ethical use, and their training needs among undergraduate students of Language Studies and Law at Sohar University in Oman.
Methods: A comparative mixed-methods design was used, with two faculty-specific questionnaires (in English for Language Studies and in Arabic for Law) harmonized into a single dataset. After non-consenting respondents were excluded, the final sample included students from both faculties. Quantitative analysis involved descriptive statistics, Welch’s t-tests, chi-square tests, and Cohen’s d. Qualitative analysis used bilingual, keyword-assisted thematic analysis of three open-ended questions.
Results: Both faculties showed a high prevalence of artificial intelligence use; however, Language Studies students reported a wider range of applications, such as grammar checking, drafting, brainstorming, and translation. Law students showed a significantly better understanding of artificial intelligence and of the ethics of responsible use. Formal training was rare in both faculties, especially in Law.
Conclusion: Use of artificial intelligence is common among students, but its frequency and purposes vary by field: Language Studies students use it more and for more tasks, while Law students are better aware of its responsible use. Institutional management of artificial intelligence should therefore be field-specific and explicit about ethics, keeping authorship with students, requiring disclosure, and teaching verification.

Keywords: artificial intelligence; generative artificial intelligence; higher education; language studies; responsible use of artificial intelligence.

Abbreviations:
AI – artificial intelligence
RQ – research question
SD – standard deviation.