Yanlin Chen is currently a Ph.D. candidate at China Agricultural University, specializing in the interdisciplinary field of artificial intelligence and educational behavioral science. His research primarily focuses on deep learning-based instructional behavior recognition and dynamic assessment systems, with specific expertise in multimodal data fusion, EEG signal analysis, and personalized teaching modeling using large language models (LLMs). His work aims to advance automated, intelligent, and interpretable approaches to education assessment. In recent years, Yanlin has conducted systematic research centered around a “cognition–behavior–feedback” feedback loop in instructional contexts. In his published paper titled “Application of Electroencephalography Sensors and Artificial Intelligence in Automated Language Teaching” (Sensors, 2024), he proposed an automated teaching assessment system that integrates EEG sensors with differential LLMs. This system captures learners’ attention levels and cognitive states in real time and dynamically adapts teaching content based on individual profiles. Experimental results demonstrated superior performance across key evaluation metrics, including a precision of 0.96, recall of 0.95, accuracy of 0.96, and an F1-score of 0.95, outperforming mainstream automated teaching models. This work provides a robust theoretical and empirical foundation for personalized and efficient instruction using neuro-AI integration.