AI literacy and subjective experience with a GPT-based virtual tutor in undergraduate physiotherapy students: a cross-sectional study.
Abstract
Objective: To analyze the association between self-reported conditions of use of a GPT-based virtual tutor and subjective learning experience in undergraduate physiotherapy students, considering academic motivation as the primary outcome. Methods: A cross-sectional analytical observational study was conducted in a 2025 cohort of undergraduate physiotherapy students exposed to a customized GPT tutor implemented as a complementary learning resource. A structured survey was used to assess seven subjective experience dimensions: motivation, self-efficacy, perceived learning facilitation, reduction of academic anxiety/stress, perceived quality, interaction/usability, and acceptance/perceived value. Three self-reported conditions of use were evaluated: tutor usage intensity, self-reported AI literacy measured through a single item, and academic verification behaviors. Associations were analyzed using bivariate Pearson correlations. Academic motivation was considered the primary outcome; secondary outcomes were adjusted for multiplicity using the Benjamini-Hochberg false discovery rate. Results: Twenty-three students were included. The three self-reported conditions of use were positively associated with academic motivation: usage intensity (r = 0.703; 95% CI 0.409 to 0.865; p < 0.001), self-reported AI literacy measured through a single item (r = 0.767; 95% CI 0.519 to 0.896; p < 0.001), and academic verification behaviors (r = 0.773; 95% CI 0.530 to 0.899; p < 0.001). For secondary outcomes, associations were positive and remained statistically significant after FDR adjustment. Final course grade was described only as contextual information, without drawing inferences about academic effectiveness. Conclusions: In this small cohort, self-reported conditions of use of a GPT-based virtual tutor were positively associated with a more favorable subjective experience. These findings should be interpreted as exploratory, contextual, and self-reported associations, without inferring causality or effectiveness on academic performance. Future studies should consider larger samples, longitudinal or controlled designs, validated instruments, and objective interaction logs.
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