ArticleFrontiers in psychology2026
AI-contextualized self-efficacy and exercise engagement among vocational college students in China: a cross-sectional serial mediation analysis of exercise self-efficacy and exercise outcome expectations.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: It remains unclear whether artificial intelligence-contextualized self-efficacy, reflecting general efficacy beliefs about coping with difficulties and achieving goals with artificial intelligence support, is associated with exercise engagement. This study examined this association and specific and serial statistical indirect effects involving exercise self-efficacy and exercise outcome expectations. Methods: A cross-sectional questionnaire survey was conducted among 2,279 students recruited by convenience sampling from one vocational college in China. Participants completed self-report measures of artificial intelligence-contextualized self-efficacy, exercise self-efficacy, exercise outcome expectations, and exercise engagement. Confirmatory factor analysis examined the measurement model. PROCESS Models 4 (preliminary) and 6 (primary) evaluated indirect effects using 5,000 bootstrap resamples and percentile 95% confidence intervals. Supplementary latent-variable structural equation models examined the theory-specified mediator ordering and its reversal. All regression and structural models adjusted for gender, age, and grade. Results: In the main PROCESS analyses, artificial intelligence-contextualized self-efficacy was positively associated with exercise engagement in the total-association model ( Conclusion: In this convenience sample, artificial intelligence-contextualized self-efficacy was associated with self-reported exercise engagement, with statistical indirect effects involving exercise self-efficacy and exercise outcome expectations. These minimally adjusted cross-sectional findings do not establish temporal ordering, causal mechanisms, or effects of actual artificial intelligence use.
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