Construction of intelligent evaluation model forphysical education classroom in primary andsecondary schools based on posture estimation andmotion recognition

In the evolving landscape of intelligent education systems, there is an urgent need to develop adaptive, transparent, and robust classroom analytics solutions that align with the priorities of human-centered artificial intelligence and multimodal interaction, as emphasized by the scope of this special issue. Existing physical activity evaluation tools for educational settings often lack scalability, context sensitivity, and the capacity to extract meaningful temporal patterns from high-dimensional behavior streams. Traditional methods tend to oversimplify the complexities of pedagogical dynamics, resulting in feedback that is static, ambiguous, or divorced from instructional intent. These challenges are met through an integrated solution that combines semantic alignment with hierarchical attention—where spatial-temporal patterns are first captured through layered attention modeling, and then adaptively contextualized to align with instructional objectives. Our system, built upon the KINEVAL architecture and the Pedagogical Contextualization Strategy (PCS), fuses motion trajectory embeddings, instructional state encoding, and environment-aware modulation to generate structured, interpretable evaluations of student performance. The inclusion of attention-based dilated GRUs, transformer-based pedagogical modeling, and peer-aware regularization not only enhances robustness and interpretability but also enables cross-domain generalization across diverse school contexts. Experimental validation shows substantial improvements over conventional baselines in accuracy, fairness, and alignment with expert annotations. This study contributes a scalable and pedagogically informed approach to classroom behavior analysis, directly supporting the special issue’s themes of intelligent sensing, adaptive learning, and multimodal system design.