Authors
Jiao Cao, Miaomiao Xu, Xiongyong Zhu, Qiusheng Long
Published in
Frontiers in physiology. Volume 17. Pages 1853479. Epub Jul 29, 2026.
Abstract
Exercise Physiology is a core course in the physical education major at higher education institutions. In the context of the modern era, leveraging generative artificial intelligence (AI) to reform its teaching framework is crucial for cultivating specialized professionals and advancing quality-oriented education. Addressing three major challenges in first-year university exercise physiology instruction-complex and interconnected teaching mechanisms that hinder systematic understanding, insufficient intuitive connections between microscopic mechanisms and macroscopic manifestations, and inadequate development of creativity-driven problem-solving skills grounded in physiological principles-the study employed AI agents and related technologies to innovate teaching philosophies, content delivery, instructional processes, and assessment methods. An innovative "three-phase, six-cycle" blended learning model was implemented. After one semester of practical implementation(16 weeks), evaluation through questionnaires and exam scores demonstrated that empowerment of Artificial Intelligence into blended teaching in Exercise Physiology significantly enhanced first-year students' critical thinking, interdisciplinary competencies, and academic performance. These findings indicated promising applications of AI in exercise physiology education. With well - designed guidance strategies and human supervision, AI agents can serve as effective teaching assistants in higher education, freeing instructors from repetitive tasks to focus on more innovative and personalized interactive instruction.
PMID:
42591087
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.
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