Use AI to support autonomy-supportive task adaptation in physical education rather than algorithmic exercise prescription¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that AI help practitioners adapt "intensity, task complexity, group size, feedback frequency, and recovery options without turning exercise into algorithmic prescription or coercive monitoring". This implements the self-determination and ecological-dynamics reasoning that sustained engagement depends on autonomy, competence, and relatedness, and that exercise should remain a psychologically safe, motivationally sustainable context for practicing regulation.

Design Implications¶

Context¶

Requirements¶

• Human judgment and student choice remain primary
• Feedback should scaffold self-regulation rather than replace student judgment

Constraints¶

• Recommendations should not become coercive exercise prescriptions

Target Learners¶

• university students in physical education classes and campus sport

Target Learning Goals¶

• emotion-regulation practice through embodied activity
• motivationally sustainable participation

Affordances¶

• Human Technology Environment Framework Emotion Regulation

Related Strategies¶

• Embed AI tools in peer, campus, and counseling ecologies instead of isolated individual apps

Examples¶

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Key Sources¶

• Zhang Yuze, Hadier Syed Ghufran, Liu Yinghai, Guo Yanlan. (2026). Contextualizing AI-Supported Emotion Regulation Through Sport and Exercise in Higher Education: A Conceptual Reframing. Behavioral Sciences. https://doi.org/10.3390/bs16071173