Do perceptions and usage of AI tools differ across genders, academic level, and fields of study? A cross-sectional study of Health Sciences students

Authors

  • Muteb Alshammari Department of Health Informatics, School of Public Health and Health Informatics, Hail University, Hail, Saudi Arabia.

DOI:

https://doi.org/10.22317/jcms.v12i1.2107

Keywords:

Artificial Intelligence, Perception, Attitude to Computers, Health Occupations, Students, Saudi Arabia

Abstract

Objective: The research examines differences in the perception, awareness, and utilization of AI tools among health sciences students in the Kingdom of Saudi Arabia (KSA). These differences are examined by gender, academic level, and field of study.

Methods: A cross-sectional quantitative survey study was conducted among Health sciences students. The study used a close-ended questionnaire at the University of Hail (UOH), KSA. The results examined general perceptions of AI and its association with learning and performance. The study also addressed ethics and academic integrity related to AI, and AI usage concepts. Data were collected from a total sample of 392 students. Descriptive statistics summarized participant characteristics and construct scores. Mann Whitney U tests were used to compare gender differences, while Kruskal Wallis H tests were adopted to assess differences across academic levels and programs.

Results: The analysis found significant differences in AI perception, ethical awareness, and use based on gender and academic level (p-value < 0.01). Additionally, ethical awareness and AI use differed across academic programs. However, general perceptions of AI showed weak but statistically significant differences among academic fields. Overall, demographic and academic factors were associated with how students perceive, evaluate ethically, and use AI technologies.

Conclusion: The study suggests the importance of incorporating of structured AI education with ethics training in health sciences curricula. The results may inform curriculum development and educational planning in KSA.

References

Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, bin Saleh K, et al. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy. 2023;19(8):1236-42.

Fahim YA, Hasani IW, Kabba S, Ragab WM. Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. European Journal of Medical Research. 2025;30(1):848.

Khine MS. AI in Teaching and Learning and Intelligent Tutoring Systems. In: Khine MS, editor. Artificial Intelligence in Education: A Machine-Generated Literature Overview. Singapore: Springer Nature Singapore; 2024. p. 467-570.

Rahman MA, Moayedikia A, Wiil UK. Editorial: Data-driven technologies for future healthcare systems. Frontiers in Medical Technology. 2023;Volume 5 - 2023.

de Bruijn H, Warnier M, Janssen M. The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly. 2022;39(2):101666.

Dhirani LL, Mukhtiar N, Chowdhry BS, Newe T. Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review. Sensors [Internet]. 2023; 23(3):[1151 p.].

Moldt J-A, Festl-Wietek T, Fuhl W, Zabel S, Claassen M, Wagner S, et al. Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education. JMIR Med Educ. 2024;10:e58355.

Kauttonen J, Rousi R, Alamäki A. Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis. J Med Internet Res. 2025;27:e65567.

Camilleri MA. Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems. 2024;41(7):e13406.

Elyoseph Z, Levkovich I, Shinan-Altman S. Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public. Family medicine and community health. 2024;12(Suppl 1).

Zhai C, Wibowo S, Li LD. The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments. 2024;11(1):28.

Davies A, Wellard-Cole L, Rangan A, Allman-Farinelli M. Validity of self-reported weight and height for BMI classification: A cross-sectional study among young adults. Nutrition. 2020;71:110622.

Maier C, Thatcher JB, Grover V, Dwivedi YK. Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. International Journal of Information Management. 2023;70:102625.

Stöhr C, Ou AW, Malmström H. Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence. 2024;7:100259.

Roni SM, Djajadikerta HG. Data analysis with SPSS for survey-based research: Springer; 2021. 1-264 p.

Izah SC, Sylva L, Hait M. Cronbach;s Alpha: A Cornerstone in Ensuring Reliability and Validity in Environmental Health Assessment. ES Energy and Environment. 2024;23:1057.

Tsagris M, Pandis N. Multicollinearity. American Journal of Orthodontics and Dentofacial Orthopedics. 2021;159(5):695-6.

Subaveerapandiyan A, Mvula D, Ahmad N, Taj A, Ahmed MG. Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice. Journal of Health Organization and Management. 2024.

Roganović J, Radenković M, Miličić B. Responsible Use of Artificial Intelligence in Dentistry: Survey on Dentists’ and Final-Year Undergraduates’ Perspectives. Healthcare [Internet]. 2023; 11(10):[1480 p.].

Shahid Satar M, Alarifi G, Alkhoraif AA, Asad M. Influence of perceptual and demographic factors on the likelihood of becoming social entrepreneurs in Saudi Arabia, Bahrain, and United Arab Emirates an empirical analysis. Cogent Business & Management. 2023;10(3):2253577.

Chu CH, Nyrup R, Leslie K, Shi J, Bianchi A, Lyn A, et al. Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults. The Gerontologist. 2022;62(7):947-55.

Franco DSouza R, Surapaneni KM, P S, Regupathy A, Mathew M, Mishra V, et al. Convergence of Diverse Expertise: A Multidisciplinary Training on the Ethics of Artificial Intelligence in Healthcare Technology and Research. Journal of Academic Ethics. 2025;23(3):885-99.

Downloads

Published

2026-02-26

How to Cite

Alshammari, M. (2026). Do perceptions and usage of AI tools differ across genders, academic level, and fields of study? A cross-sectional study of Health Sciences students. Journal of Contemporary Medical Sciences, 12(1), 23–28. https://doi.org/10.22317/jcms.v12i1.2107