Previously submitted to: JMIR Medical Education (no longer under consideration since Mar 02, 2026)
Date Submitted: Mar 2, 2026
Open Peer Review Period: Mar 2, 2026 - Mar 2, 2026
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Modeling pedagogical architecture in artificial intelligence–mediated clinical reasoning: Preliminary observational study in undergraduate physiotherapy education
ABSTRACT
Background:
Artificial intelligence (AI) is increasingly integrated into health professions education. However, most empirical studies focus on tool adoption or learner satisfaction rather than on the structural relationships between pedagogical design, perceived cognitive impact, and student endorsement. Understanding how AI-mediated instructional components interact within a coherent educational framework is essential to determine whether AI supports higher-order clinical reasoning rather than merely enhancing technological appeal.
Objective:
This preliminary study aimed to develop and empirically evaluate a theory-driven AI-mediated clinical reasoning framework in undergraduate physiotherapy education and to examine the structural relationships among pedagogical domains, perceived learning impact, and student satisfaction.
Methods:
An observational mixed-methods educational evaluation was conducted during full-course implementation of an AI-mediated framework in a third-year undergraduate physiotherapy course. The complete academic cohort (n = 22) completed a 24-item post-intervention questionnaire assessing 7 pedagogical domains on a 5-point Likert scale. Descriptive statistics were calculated for all domains. Two-tailed Pearson correlations were used to examine associations with satisfaction. Exploratory multiple linear regression was performed to assess potential independent associations among domains. An exploratory factor analysis examined dimensional alignment with the theoretical framework. Academic performance indicators were summarized descriptively.
Results:
Students reported high ratings across domains, with mean scores exceeding 4.5/5 for all constructs. The strongest association with satisfaction was observed for perceived learning impact (r = 0.79, p < .001; 95% CI 0.55–0.91). Moderate-to-large correlations were also found for usability (r = 0.66, p = .001), AI content quality (r = 0.61, p = .002), pedagogical coherence (r = 0.58, p = .004), critical thinking (r = 0.52, p = .012), and ethical integration (r = 0.47, p = .028). In exploratory multivariable modeling, the overall regression model accounted for 39.4% of the variance in satisfaction (R² = 0.394; F(6,15) = 1.62, p = .208), although no predictor reached statistical significance after adjustment. Exploratory factor analysis suggested a multidimensional configuration broadly consistent with cognitive, technological-experience, and pedagogical-ethical domains. Academic performance indicators demonstrated high pass rates exceeding 90% across assessment components.
Conclusions:
Within this preliminary cohort, perceived learning impact emerged as the central explanatory mechanism linking AI-mediated instructional design to student satisfaction. Although multivariable modeling was exploratory and limited by sample size, the findings suggest that the educational value of AI in physiotherapy training depends on its alignment with cognitively demanding reasoning processes rather than technological usability alone. These results provide preliminary structural support for AI-mediated clinical reasoning frameworks and warrant confirmation in larger, multicenter investigations.
Citation
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