Acute physiological and perceptual responses in the FIT FIRST FOR ALL school-based physical activity program.

In contemporary education, the coordinated development of physical and mental health has become a central goal. Physical education (PE) is no longer limited to physical training and the transmission of motor skills; it also plays a crucial role in cultivating students’ psychological well-being. During PE learning, students experience various emotional responses and may face psychological challenges such as low self-confidence or poor teamwork. These issues directly affect their learning outcomes and can hinder their holistic physical and mental development. In response, educational authorities in China have emphasized the deep integration of physical and psychological education, with policy documents highlighting the need to improve social psychological service systems. However, in practice, integrating PE with psychological education remains challenging. Traditional PE has long focused on physical performance standards and movement accuracy, often overlooking opportunities for psychological guidance embedded in physical activities. Many PE teachers lack formal training in psychology, making it difficult to accurately detect students’ emotional changes or provide personalized interventions tailored to individual psychological profiles. The model designed in this study takes into full account the practical requirements of frontline teaching. Instead of producing complex feature vectors or technical algorithmic parameters, it presents results in a visualized and interpretable format, including the classification of students’ psychological states, dominant emotional tendencies, and corresponding simplified intervention suggestions. This design enables PE teachers to quickly understand students’ psychological conditions in PE classes without requiring specialized knowledge in psychology. Existing psychological education programs often follow rigid, standardized formats, relying on manual observation or paper-based questionnaires. These approaches are inefficient, prone to subjective bias, and unable to identify students’ latent psychological risks in a timely manner, thus failing to meet the growing demand for personalized psychological education in modern PE. Psychological assessment in traditional PE predominantly depends on manual observation conducted by instructors, which introduces constraints in representational granularity. Such a mechanism complicates the quantitative characterization of student emotional fluctuations and limits the precise detection and intervention of psychological states specific to sports-related contexts. Within PE classrooms, students often experience reduced self-confidence associated with delayed acquisition of motor skills and suboptimal athletic performance. In parallel, cooperative learning tasks and team-based competitions may trigger interpersonal coordination barriers and communication conflicts, while endurance training and physical challenges frequently induce anxiety responses and avoidance tendencies toward difficulty-oriented tasks. These manifestations exhibit contextual structures that differ markedly from psychological distress patterns observed in conventional academic learning environments. Conventional analytical paradigms remain insufficient in capturing real-time psychological dynamics during physical activity, and personalized intervention strategies tailored to sports-specific contexts are similarly underdeveloped. This gap produces a structural disconnect between psychological education methodologies and PE practice scenarios. To mitigate these limitations, the proposed model is designed to address representative psychological issues in PE settings, including diminished self-confidence, impaired teamwork interaction, and sports-related anxiety. The framework enables computational recognition of emotional states and psychological conditions at the textual representation level, thereby compensating for the lack of context-sensitive assessment in traditional evaluation approaches. With the rapid advancement of artificial intelligence (AI), its applications in education have expanded, providing new approaches and technical support for integrating PE with psychological education. AI can efficiently process large-scale educational data and uncover hidden insights, overcoming the limitations of traditional teaching and enabling precise, individualized psychological interventions. In this context, integrating AI into psychological education within PE and constructing scientifically grounded, efficient models has become a critical path to addressing current challenges and promoting high-quality development in PE. Natural Language Processing (NLP), a key branch of AI, enables deep analysis and understanding of textual data. In PE, students’ verbal expressions, written reflections, and learning feedback provide rich text-based evidence of their psychological states. This study selects four key technologies and leverages their respective strengths to construct a multi