Contextualizing AI-Supported Emotion Regulation Through Sport and Exercise in Higher Education: A Conceptual Reframing.
Artificial intelligence (AI) is being increasingly used in educational and mental health contexts, yet many emotion-related applications still prioritize detection, classification, and automated feedback over contextual understanding. This study uses the Stanford AI Index Reports (2021-2025) as an exploratory discourse corpus to examine how prominent AI reports frame technology, application domains, and governance, and to consider what this framing implies for emotion regulation through sport and exercise in higher education.
Across 1813 report pages, we applied BERTopic with multilingual sentence embeddings (paraphrase-multilingual-MiniLM-L12-v2), UMAP dimensionality reduction, HDBSCAN clustering, and class-based TF-IDF, followed by dynamic and hierarchical topic analysis and theory-informed synthesis. Of 22 topics generated, 13 relevant to the study focus were retained and validated through keyword inspection, representative-text review, and independent expert agreement.
The analysis indicated a three-layer structure: a technology core, an application-expansion layer, and an ethics-and-governance layer. Health and education themes grew most across reports, with medicine/health rising from 14 to 105 and school pathways from 3 to 105 segment occurrences between 2021 and 2025, whereas sport, exercise, embodied activity, and campus support appeared only indirectly. As prominence reflects raw frequency across five reports, trends are read descriptively.
We propose a human-technology-environment framework comprising multimodal contextual profiling, autonomy-supportive task adaptation, feedback-reflection-practice loops, peer and campus support integration, and human-in-the-loop governance. The study does not test intervention effects; its contribution is conceptual and agenda-setting, clarifying a gap between mainstream AI discourse and the embodied, relational, and ecological conditions through which sport and exercise may support students' emotion regulation.
Across 1813 report pages, we applied BERTopic with multilingual sentence embeddings (paraphrase-multilingual-MiniLM-L12-v2), UMAP dimensionality reduction, HDBSCAN clustering, and class-based TF-IDF, followed by dynamic and hierarchical topic analysis and theory-informed synthesis. Of 22 topics generated, 13 relevant to the study focus were retained and validated through keyword inspection, representative-text review, and independent expert agreement.
The analysis indicated a three-layer structure: a technology core, an application-expansion layer, and an ethics-and-governance layer. Health and education themes grew most across reports, with medicine/health rising from 14 to 105 and school pathways from 3 to 105 segment occurrences between 2021 and 2025, whereas sport, exercise, embodied activity, and campus support appeared only indirectly. As prominence reflects raw frequency across five reports, trends are read descriptively.
We propose a human-technology-environment framework comprising multimodal contextual profiling, autonomy-supportive task adaptation, feedback-reflection-practice loops, peer and campus support integration, and human-in-the-loop governance. The study does not test intervention effects; its contribution is conceptual and agenda-setting, clarifying a gap between mainstream AI discourse and the embodied, relational, and ecological conditions through which sport and exercise may support students' emotion regulation.