A review of learner posture recognition for smart education

Purpose Learner posture is a key non-verbal behavioral cue in smart education, reflecting engagement, cognitive state, emotional response and collaboration quality. While posture recognition has been widely studied in computer vision, its systematic integration into education research remains limited. This paper aims to provide the first comprehensive review of learner posture recognition from both technological and educational perspectives, thereby establishing a conceptual and methodological foundation for future intelligent teaching systems. Design/methodology/approach Adopting a systematic literature review, we analyze key aspects of posture recognition – including representation models, keypoint detection, data collection, multimodal fusion and deployment strategies – while explicitly situating them within situational cognition theory. Unlike prior studies that emphasize technical performance alone, this review examines how posture recognition technologies can inform educational interpretation and intervention. Findings Posture recognition supports real-time teaching feedback, personalized assistance and collaborative analysis. However, comparative evaluation with existing automated interaction systems and situationally aware design frameworks remains scarce, limiting the ability to assess substantive breakthroughs. By synthesizing current advances and identifying these gaps, the review clarifies both the state of the art and the pathways toward robust, theory-informed applications. Research limitations/implications Rather than proposing yet another incremental model, this paper contributes originality through (1) offering the first taxonomy of posture recognition tailored to smart education, (2) bridging technical modeling with educational theory and (3) identifying critical gaps in benchmarking and comparative evaluation. These contributions provide an essential reference for advancing posture perception as an interpretable and educationally meaningful component of smart learning environments. Originality/value This paper systematically integrates the research context and key technologies of learner posture recognition in smart education for the first time, proposes future development directions and has important reference value for promoting the integration and innovation of posture perception and intelligent teaching.