We developed a method for updating and constructing Chinese language teaching resources by integrating sensor technology and knowledge graphs.The method addresses the challenges of a mismatch between resource supply and learner demand through a closed loop of perceptionanalysis-update. Wearable sensors and AI cameras were used to collect real-time, quantitative multimodal data on learners' physiological and behavioral states, including body temperature, heart rate, and classroom interactions.These sensor data, along with learning platform data, were used to train an eXtreme Gradient Boosting model.The model achieved a prediction accuracy of 89.2% and an area under the receiver operating characteristic curve of 0.93, indicating the accurate distinction of knowledge entities that need updating and those that do not.The feature importance analysis revealed that user ratings (0.603) and recency (0.226) were the most influential factors for predicting update necessity.The knowledge graph was iteratively updated through a multistep process including pattern mining, filtering, and a final review by experts.The resulting knowledge graph incorporated nodes for new content, such as internet slang and cross-cultural variations in festivals, demonstrating the method's ability to adapt to linguistic evolution and cultural nuances.Through the establishment of a closed-loop architecture, multimodal sensor data, including physiological photoplethysmography signals and behavioral time-of-flight imaging, are used for the expansion and weight adjustment of a domain-specific knowledge graph.This cognitive-aware update mechanism ensures that learning resources evolve along with real-time learner demands, providing a scalable blueprint for intelligent, sensor-driven knowledge management systems in various disciplines.The results of this study also underscore the role of sensor data in developing contextualized, personalized, and optimized digital learning resources that lead to a learner-centered learning environment.In the AI era, digital learning resources have become fundamental for the quality enhancement of Chinese language education. (6)Research on effective Chinese language education has been extensively conducted, (7) in which classification systems of Chinese learning resources have been developed to construct learning resources and databases. (8)With the application of wearable sensors, edge computing devices, IoT, and knowledge graphs, learning resources can be further developed on the basis of new technological approaches and paradigms.Knowledge graphs, in the form of a formal semantic network of nodes, edges, and attributes, are used to model Chinese characters, vocabulary, grammar, cultural concepts, and their dynamic interrelations, for the scalable organization of learning resources. (9)Sensors are employed to capture cognitive, emotional, and behavioral signals of learners in real or virtual contexts through data collection at a millisecond-level interval, enabling high-resolution and contextualized resource updates.While knowledge graphs are used to present static knowledge, sensor data are used to analyze learning patterns and assess the learner's attitude and responses.However, knowledge graphs and sensor data have been used separately, which hinders the integration required to construct a closed-loop framework that enables a positive feedback cycle among the knowledge graph, sensor perception, and learning resources.Therefore, we studied how to leverage the synergy of knowledge graphs and sensor technology in constructing Chinese language learning resources through the real-time collection of multimodal data on learners' physiology, behavior, and cognition, and the data analytics through adaptive iteration and personalized adaptation.In this study, a sensor-driven cognitively adaptive system and its underlying architecture were constructed for real-time data perception using wearable and ambient sensors, predictive cognitive modeling, and automated knowledge
Fu et al. (Mon,) studied this question.