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Background: AI-based chatbots are increasingly used in health professions education to support learning tasks (e.g., studying, drafting assignments, and summarizing content). Evidence on the acceptance of AI-based chatbots, including ChatGPT, in physiotherapy education is limited. This study examined physiotherapy students’ acceptance in Turkey, focusing on perceived usefulness (PU) and perceived ease of use (PEOU) within the Technology Acceptance Model (TAM). Methods: A cross-sectional online survey (Google Forms) using convenience sampling was conducted with undergraduate physiotherapy students from nine universities across different regions of Turkey (May–June 2025). Digital informed consent was obtained, and responses were anonymous. PU and PEOU were assessed using Turkish TAM items contextualized for AI-based chatbot use. Psychometric properties were re-evaluated in the present sample (Cronbach’s alpha; PCA with KMO/Bartlett, with an EFA sensitivity analysis using PAF with Promax rotation), and group differences, correlations, and predictors of PU and PEOU were analyzed. Results: A total of 478 students (79.3% female) were included. Mean PU and PEOU scores were 52.91 ± 7.47 and 51.73 ± 6.49, respectively (scale range 14–70). PU and PEOU were moderately correlated (r = 0.450, p < 0.001). Technology interest was the strongest predictor of PU (β = 0.283, p < 0.001) and PEOU (β = 0.371, p < 0.001), while year of study had a smaller effect on PU (β = 0.112, p = 0.011); gender was not significant in regression models. Exploratory factor-analytic results suggested PU was unidimensional, whereas PEOU showed two exploratory facets (learnability/clarity and control/flexibility). Conclusions: Physiotherapy students expressed cautious optimism toward AI-based chatbots such as ChatGPT. Acceptance was higher among students with greater technology interest and, to a lesser extent, in advanced study years. These findings support stage-sensitive integration and targeted AI literacy, emphasizing ethical awareness and source verification for safe and effective learning.
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Article
Belge Türü
Kaynak: BMC MEDICAL EDUCATION
Anahtar Kelimeler (WoS)
Havuzumuzdaki Atıflar 0
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Anahtar Kelimeler
Artificial intelligence
ChatGPT
Digital learning tools
Educational technology
Higher education
Physiotherapy education
Technology acceptance model
WoS |
Bir kelimeye tıklayıp ilgili kaynaktaki yayınları görün.
Makale Bilgileri
Dergi
BMC MEDICAL EDUCATION
ISSN
1472-6920
Yıl
2026
/ 1. ay
Cilt / Sayı
26
/ 1
Sayfalar
1 – 11
Makale Türü
Özgün Makale
Hakemlik
Hakemli
Endeks
SCI-Expanded
JCR Quartile
Q1
Teşvik Puanı
3,60
· YÖKSİS Akademik Teşvik
Yayın Dili
İngilizce
Kapsam
Uluslararası
Toplam Yazar
5 kişi
Erişim Türü
Elektronik
Alan
Sağlık Bilimleri Temel Alanı
Fizyoterapi ve Rehabilitasyon
YÖKSİS Yazar Kaydı
Yazar Adı
GÜLER MEHMET AKİF,Dağ Sümeyra,Kayal Senem,Can Merve,Şimşekkaya Sena Nur
YÖKSİS ID
9330757