Background: encounters difficulties in emotional sensitivity and personalized adaptation.Objective: This study aimed to investigate the effectiveness of an AI emotion recognition feedback-based: Anxiety prevalence is high in cancer survivors, and the traditional physical education teaching physical education teaching system in improving anxiety levels and stimulating exercise motivation among cancer survivors. Methods: The randomized controlled trial used a stratified block randomization procedure (by cancer type and baseline GAD-7 tertile) with sealed-envelope allocation concealment and blinded outcome assessment, randomly assigning cancer survivors into an AI intervention group or a traditional teaching control group. A 12-week progressive exercise intervention was implemented, integrating multimodal emotion recognition with adaptive feedback mechanisms based on self-determination theory and emotion regulation theory. Results: The AI intervention group showed greater anxiety reduction than the control group, with larger within-group effect sizes and higher rates of clinically meaningful improvement; exercise motivation shifted toward intrinsic regulation, and exploratory structural equation modelling was compatible with an “AI feedback → intrinsic motivation → exercise behaviour → anxiety improvement” pathway, in which intrinsic motivation served as a significant mediator. Conclusion: The AI emotion recognition feedback teaching system appears to extend traditional instructional practice through real-time emotional monitoring and personalized feedback. These preliminary findings warrant replication in multi-centre trials with larger samples, longer follow-up, and active comparators before broader claims can be drawn, and may inform the design of emotion-aware intervention models for related populations pending dedicated validation.
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Zhang et al. (2026) studied this question.
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