Integrating generative AI into physiotherapy education: students’ use, perceptions, and generative AI literacy following curricular adaptations: a repeated cross-sectional programme evaluation

Abstract Background Structured generative artificial intelligence (GAI) training has been proposed as an effective approach to strengthen healthcare students’ confidence in navigating AI-related challenges and ethical considerations. However, few studies have evaluated the impact of implemented GAI-related curricular adaptations and learning activities, particularly within physiotherapy education. This study aimed to evaluate: 1) physiotherapy students’ use and perceptions of GAI in their studies during implementation of GAI-related curricular adaptations; and 2) GAI literacy in semester 1 before participation and in semester 3 after completion of structured GAI learning activities. Methods A two-part repeated cross-sectional programme evaluation with non-equivalent, unlinked samples was conducted among physiotherapy students at Linköping University, Sweden. Students’ use and perceptions of GAI were assessed after implementation of GAI-related curricular adaptations in 2024 and again in a separate survey wave in 2025. GAI literacy was evaluated using the 10-item Generative Artificial Intelligence Literacy Scale (GAILS-10) before and after the GAI learning activities delivered during semesters 1–3. Results Reported GAI use differed significantly between 2024 and 2025 surveys, χ 2 (3) = 62.40, p < 0.001, with daily use reported by 57% of respondents in the 2025 survey compared with 13% in the 2024 survey. Respondents in the 2025 survey reported more diverse use of GAI, including brainstorming, question generation, and text refinement ( p < 0.001). Perceptions of GAI as contributing to learning were also more positive in the 2025 survey, χ 2 (4) = 24.94, p < 0.001, whereas perceptions of information about permissible and impermissible use were similar in both surveys. Semester 3 students assessed after completing the GAI learning activities reported higher GAILS-10 scores across all dimensions than semester 1 students assessed before participation, with a large between-group effect size for the total score ( d = 1.0). Conclusion Physiotherapy students reported more frequent and diverse GAI use and more positive learning perceptions in the 2025 survey than in the 2024 survey. Semester 3 students, assessed after completing the GAI learning activities, reported higher GAI literacy than semester 1 students assessed before participation in these activities. These findings suggest a need for curricular adaptations and educational support to promote appropriate GAI use, reduce misuse, and strengthen student GAI literacy.

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Journal
BMC Medical Education
Published
2026-08-28
DOI
https://doi.org/10.1186/s12909-026-10256-0
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrating generative AI into physiotherapy education: students’ use, perceptions, and generative AI literacy following curricular adaptations: a repeated cross-sectional programme evaluation

Yvonne Lindbäck, Karin Schröder, Sofi Sonesson, Torkel Engström
BMC Medical Education
Artificial Intelligence in Healthcare and Education
article

Integrating generative AI into physiotherapy education: students’ use, perceptions, and generative AI literacy following curricular adaptations: a repeated cross-sectional programme evaluation

Yvonne Lindbäck, Karin Schröder, Sofi Sonesson, Torkel Engström
article en

Abstract

Abstract Background Structured generative artificial intelligence (GAI) training has been proposed as an effective approach to strengthen healthcare students’ confidence in navigating AI-related challenges and ethical considerations. However, few studies have evaluated the impact of implemented GAI-related curricular adaptations and learning activities, particularly within physiotherapy education. This study aimed to evaluate: 1) physiotherapy students’ use and perceptions of GAI in their studies during implementation of GAI-related curricular adaptations; and 2) GAI literacy in semester 1 before participation and in semester 3 after completion of structured GAI learning activities. Methods A two-part repeated cross-sectional programme evaluation with non-equivalent, unlinked samples was conducted among physiotherapy students at Linköping University, Sweden. Students’ use and perceptions of GAI were assessed after implementation of GAI-related curricular adaptations in 2024 and again in a separate survey wave in 2025. GAI literacy was evaluated using the 10-item Generative Artificial Intelligence Literacy Scale (GAILS-10) before and after the GAI learning activities delivered during semesters 1–3. Results Reported GAI use differed significantly between 2024 and 2025 surveys, χ 2 (3) = 62.40, p < 0.001, with daily use reported by 57% of respondents in the 2025 survey compared with 13% in the 2024 survey. Respondents in the 2025 survey reported more diverse use of GAI, including brainstorming, question generation, and text refinement ( p < 0.001). Perceptions of GAI as contributing to learning were also more positive in the 2025 survey, χ 2 (4) = 24.94, p < 0.001, whereas perceptions of information about permissible and impermissible use were similar in both surveys. Semester 3 students assessed after completing the GAI learning activities reported higher GAILS-10 scores across all dimensions than semester 1 students assessed before participation, with a large between-group effect size for the total score ( d = 1.0). Conclusion Physiotherapy students reported more frequent and diverse GAI use and more positive learning perceptions in the 2025 survey than in the 2024 survey. Semester 3 students, assessed after completing the GAI learning activities, reported higher GAI literacy than semester 1 students assessed before participation in these activities. These findings suggest a need for curricular adaptations and educational support to promote appropriate GAI use, reduce misuse, and strengthen student GAI literacy.

BMC Medical EducationVol. 26(1)
Linköping University (SE)
Linköpings Universitet, Faculty of Medicine and Health, University of Sydney
Quality Education
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
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