Attitudes Toward Artificial Intelligence Among Physiotherapy and Rehabilitation Students: A Cross-Sectional Study
Abstract
Purpose: The aim of this study was to investigate the attitudes of Physiotherapy and Rehabilitation students towards Artificial İntelligence (AI) and to compare the attitudes towards AI according to sociodemographic changes.
Methods: 212 students participated in this study. Participants' demographic data were recorded using a sociodemographic data form. Students' attitudes toward AI were surveyed with the General Attitudes toward Artificial Intelligence Scale (GAAIS).
Results: It was observed that positive and negative attitude scores didn’t differ according to age, gender, class level, accommodation, income, type of high school graduated, income status, mother or father’s education level (p>0.05). However, a significant difference was found in positive attitude scores based on daily ınternet usage duration, and in negative attitude scores based on type of income (p<0.001). There was no difference in positive or negative attitude scores based on receiving training about AI, having previously used an AI-based application, or having general knowledge about AI (p>0.05). The frequency of using AI applications showed a significant difference in positive attitude scores (p=0.02).
Conclusion: Students had positive attitudes toward AI. Moreover, while students’ attitudes were not affected by sociodemographic variables toward AI, greater use of the internet and AI contributed to more positive attitudes.
Keywords
References
- 1. Majumder A, Sen D. Artificial intelligence in cancer diagnostics and therapy: current perspectives. Indian J Cancer. 2021;58(4):481-92. https://doi.org/10.4103/ijc.IJC_399_20
- 2. Shin Y. Toward Human-Centered Artificial Intelligence for Users’ Digital Well-Being: Systematic Review, Synthesis, and Future Directions. JMIR Human Factors. 2025;12(1):e69533. https://doi. org/10.2196/69533
- 3. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81. https://doi.org/ 10.1080/13645706.2019.1575882
- 4. Bhatt J, Jain S, Bhatia DD. Artificial Intelligence (Al) in Healthcare Diagnosis: Evidence-Based Recent Advances and Clinical Implications. Sens Diagn. 2025;4:1047-1059. https://doi.org/10.1039/ D5SD00146C5.
- 5. Khanna NN, Maindarkar MA, Viswanathan V et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare; 2022;10(12):2493:1-38. https://doi.org/10.3390/ healthcare101224936.
- 6. Guo Y, Wang T, Ge T, et al. Prevalence of self-care disability among older adults in China. BMC geriatrics. 2022;22(1):775. https://doi. org/10.1186/s12877-022-03412-w
- 7. Sumner J, Lim HW, Chong LS et al. Artificial intelligence in physical rehabilitation: A systematic review. Artif Intell Med. 2023;146:102693. https://doi.org/10.1016/j.artmed.2023.102693
- 8. Jack K, McLean SM, Moffett JK et al. Barriers to treatment adherence in physiotherapy outpatient clinics: a systematic review. Manual therapy. 2010;15(3):220-8. https://doi.org/10.1016/j. math.2009.12.004
Details
Primary Language
English
Subjects
Physical Medicine and Rehabilitation
Journal Section
Research Article
Publication Date
April 20, 2026
Submission Date
December 22, 2025
Acceptance Date
March 10, 2026
Published in Issue
Year 2026 Volume: 17 Number: April, May, June 2026