Salwa B El-Sobkey, Kerolous Ishak Kelini, Mahmoud ElKholy, Tayseer Abdeldayem, Mariam Abdallah, Dina Al-Amir Mohamed, Aya Fawzy, Yomna F Ahmed, Ayman El Khatib, Hind Khalid, Balkhis Banu Shaik, Ana Anjos, Mutasim D Alharbi, Karim Fathy, Khaled Takey
{"title":"物理治疗学生对人工智能聊天机器人的接受程度：国际横断面研究。","authors":"Salwa B El-Sobkey, Kerolous Ishak Kelini, Mahmoud ElKholy, Tayseer Abdeldayem, Mariam Abdallah, Dina Al-Amir Mohamed, Aya Fawzy, Yomna F Ahmed, Ayman El Khatib, Hind Khalid, Balkhis Banu Shaik, Ana Anjos, Mutasim D Alharbi, Karim Fathy, Khaled Takey","doi":"10.2196/76574","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence-powered chatbots (AI-PCs) are increasingly integrated into educational settings, including health care disciplines. Despite their potential to enhance learning, limited research has investigated physiotherapy (PT) students' acceptance of this technology.</p><p><strong>Objective: </strong>This study aims to assess undergraduate PT students' acceptance of AI-PCs and to identify personal, academic, and technological factors influencing their acceptance.</p><p><strong>Methods: </strong>Over a 4-month period, a cross-sectional survey was conducted across 7 PT programs in 5 countries. Eligible participants were national undergraduate PT students. The technology acceptance model (TAM)-based questionnaire was used for capturing perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual behavioral use of AI-PCs. The influence of personal, academic, and technological factors was examined. Descriptive and inferential statistics were conducted.</p><p><strong>Results: </strong>The mean total TAM score was 3.59 (SD 0.82), indicating moderate acceptance. Of the 1066 participants, 375 (35.2%) showed high acceptance, 650 (60.9%) moderate, and 41 (3.9%) low. Prior experience with artificial intelligence (AI) tools emerged as the strongest predictor of acceptance (β=.43; P<.001), followed by university affiliation (ANOVA P<.001). Cumulative grade point average percentage was positively correlated with TAM score (r=0.135; P<.001) but was not a significant predictor in regression (P=.23). Age (P=.54), sex (P=.56), academic level (P=.26), and current use of AI-PCs (P=.10) were not significant predictors.</p><p><strong>Conclusions: </strong>PT students demonstrated moderate acceptance of AI-PCs. Prior technological experience was the strongest predictor, underscoring the importance of early exposure to AI tools. Educational institutions should consider integrating AI technologies to enhance students' familiarity and foster positive attitudes toward their use.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e76574"},"PeriodicalIF":3.2000,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364448/pdf/","citationCount":"0","resultStr":"{\"title\":\"Acceptance of AI-Powered Chatbots Among Physiotherapy Students: International Cross-Sectional Study.\",\"authors\":\"Salwa B El-Sobkey, Kerolous Ishak Kelini, Mahmoud ElKholy, Tayseer Abdeldayem, Mariam Abdallah, Dina Al-Amir Mohamed, Aya Fawzy, Yomna F Ahmed, Ayman El Khatib, Hind Khalid, Balkhis Banu Shaik, Ana Anjos, Mutasim D Alharbi, Karim Fathy, Khaled Takey\",\"doi\":\"10.2196/76574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Artificial intelligence-powered chatbots (AI-PCs) are increasingly integrated into educational settings, including health care disciplines. Despite their potential to enhance learning, limited research has investigated physiotherapy (PT) students' acceptance of this technology.</p><p><strong>Objective: </strong>This study aims to assess undergraduate PT students' acceptance of AI-PCs and to identify personal, academic, and technological factors influencing their acceptance.</p><p><strong>Methods: </strong>Over a 4-month period, a cross-sectional survey was conducted across 7 PT programs in 5 countries. Eligible participants were national undergraduate PT students. The technology acceptance model (TAM)-based questionnaire was used for capturing perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual behavioral use of AI-PCs. The influence of personal, academic, and technological factors was examined. Descriptive and inferential statistics were conducted.</p><p><strong>Results: </strong>The mean total TAM score was 3.59 (SD 0.82), indicating moderate acceptance. Of the 1066 participants, 375 (35.2%) showed high acceptance, 650 (60.9%) moderate, and 41 (3.9%) low. Prior experience with artificial intelligence (AI) tools emerged as the strongest predictor of acceptance (β=.43; P<.001), followed by university affiliation (ANOVA P<.001). Cumulative grade point average percentage was positively correlated with TAM score (r=0.135; P<.001) but was not a significant predictor in regression (P=.23). Age (P=.54), sex (P=.56), academic level (P=.26), and current use of AI-PCs (P=.10) were not significant predictors.</p><p><strong>Conclusions: </strong>PT students demonstrated moderate acceptance of AI-PCs. Prior technological experience was the strongest predictor, underscoring the importance of early exposure to AI tools. Educational institutions should consider integrating AI technologies to enhance students' familiarity and foster positive attitudes toward their use.</p>\",\"PeriodicalId\":36236,\"journal\":{\"name\":\"JMIR Medical Education\",\"volume\":\"11 \",\"pages\":\"e76574\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364448/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/76574\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/76574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
Acceptance of AI-Powered Chatbots Among Physiotherapy Students: International Cross-Sectional Study.
Background: Artificial intelligence-powered chatbots (AI-PCs) are increasingly integrated into educational settings, including health care disciplines. Despite their potential to enhance learning, limited research has investigated physiotherapy (PT) students' acceptance of this technology.
Objective: This study aims to assess undergraduate PT students' acceptance of AI-PCs and to identify personal, academic, and technological factors influencing their acceptance.
Methods: Over a 4-month period, a cross-sectional survey was conducted across 7 PT programs in 5 countries. Eligible participants were national undergraduate PT students. The technology acceptance model (TAM)-based questionnaire was used for capturing perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual behavioral use of AI-PCs. The influence of personal, academic, and technological factors was examined. Descriptive and inferential statistics were conducted.
Results: The mean total TAM score was 3.59 (SD 0.82), indicating moderate acceptance. Of the 1066 participants, 375 (35.2%) showed high acceptance, 650 (60.9%) moderate, and 41 (3.9%) low. Prior experience with artificial intelligence (AI) tools emerged as the strongest predictor of acceptance (β=.43; P<.001), followed by university affiliation (ANOVA P<.001). Cumulative grade point average percentage was positively correlated with TAM score (r=0.135; P<.001) but was not a significant predictor in regression (P=.23). Age (P=.54), sex (P=.56), academic level (P=.26), and current use of AI-PCs (P=.10) were not significant predictors.
Conclusions: PT students demonstrated moderate acceptance of AI-PCs. Prior technological experience was the strongest predictor, underscoring the importance of early exposure to AI tools. Educational institutions should consider integrating AI technologies to enhance students' familiarity and foster positive attitudes toward their use.
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