ArticleFrontiers in sports and active living2026
Feasibility of a teacher-supervised ChatGPT-assisted workflow for individualized exercise planning in university physical education: a two-phase study using a fuzzy Delphi process and a cluster pilot trial.
Article in Frontiers in sports and active living, 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: University physical education can promote physical activity and fitness, but individualized instruction is difficult to implement in routine classes. This study developed a structured input framework for ChatGPT-assisted individualized fitness teaching and evaluated its feasibility, safety, acceptability, and preliminary effects. Methods: This two-phase study involved non-sports-major university students. Phase I used a two-round fuzzy Delphi process to develop the framework. Phase II was a 6-week cluster pilot trial involving two intact classes allocated by coin toss to either AI-assisted instruction ( Results: The final framework comprised 49 items, with 96.4% achieving a consensus rate ≥75%. Follow-up completion rates were 96.7% and 93.3% in the AI and control groups, respectively. Attendance, completion rate, and perceived exertion were similar between groups. Time in the target heart-rate zone was higher in the AI group (33.95 ± 4.33 vs. 32.60 ± 3.74 min, Conclusions: ChatGPT-assisted individualized fitness instruction was feasible, safe, and acceptable within a structured teacher-supervised workflow, with its primary short-term benefit being improved perceived personalization.
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