Exploring the Effect of Using Artificial Intelligence Tools on Preclinical Medical Students' Workload and Well-being: A Cross-Sectional Study. | ||||
| Journal of Health Professions Education and Innovation | ||||
| Volume 2, Issue 1, May 2025, Page 11-21 PDF (636.46 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jhpei.2025.347543.1040 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Eman Elrefaei   1; Omnia Safwat El-Deep2; Sarah Ragab Abd El-Khalik3; Amira Kamel Eltokhy4; Asmaa H. Okasha4; Mohamed Elsheikh5; Shereen Shamma6; Rowaida Raafat Ibrahim 7; Azza Awad Aboraya8
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| 1Medical Biochemistry department, Faculty of Medicine, Tanta University, Egypt. | ||||
| 2medical biochemistry department, faculty of medicine, tanta university | ||||
| 3Medical Biochemistry Department, Faculty of Medicine, Tanta University, Egypt | ||||
| 4Medical Biochemistry Department, Faculty of Medicine, Tanta University, Egypt. | ||||
| 5Department of Emergency Medicine, Faculty of Medicine, Tanta University, Egypt. Department of public Health, school of Medicine, Kyoto University, Japan. | ||||
| 6Department of Medical Education, Faculty of Medicine, Tanta University, Egypt. | ||||
| 7Member of Medical Education Department, Faculty of Medicine, Tanta University, El-Geesh St, Tanta, Egypt. Assistant Professor of Medical Biochemistry & Molecular Biology | ||||
| 8Histology and cell biology , Faculty of Medicine, Tanta University | ||||
| Abstract | ||||
| There is a recent rise in the application of AI in medical education research and development. We aim to assess the effects of AI tools on the workload and well-being of preclinical medical students, concentrating on their views, acceptance, and satisfaction with these technologies. A cross-sectional survey was conducted between September and October 2024. A total of 926 preclinical medical students from Tanta University, Egypt, were involved. Data were gathered using a validated online questionnaire that included sections on demographic data, perception of AI tools, the impact of AI on medical education and willingness to use it, and the impact of AI on workload and well-being. Descriptive statistical analyses of questionnaire sections were done. A logistic regression model was used to assess the association of 13 predictor questions with the effect of AI on students' well-being and their willingness to use AI. A significant P-value (P < 0.001) suggests a strong association between computer literacy and willingness to use AI. Among all predictor questions, the strongest association for a'strongly agree' response was observed for the statement 'I'm aware of AI applications in different aspects of life' (Adjusted Odds Ratios [AORs]: 3.19 [95% CI: 1.96–5.26]). Additionally, the statement 'I assume AI could replace traditional teaching methods' showed significant associations in improving well-being for both'strongly agree' (AOR: 2.24 [95% CI: 1.38–3.68]) and 'agree' (AOR: 1.55 [95% CI: 1.04–2.37]). This study highlights the significance of integrating AI technologies into medical education to improve students’ well-being and decrease workload. | ||||
| Keywords | ||||
| Medical Education; Student Well-being; Workload | ||||
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