Exploring the impact mechanisms of EEG signals and emotional intelligence levels on language learning efficiency.

Improving language learning through a better understanding of how brain activity and emotional intelligence interact is a promising research direction with practical value in education. Traditional methods in this area often use static models, which struggle to capture how cognitive and emotional states change over time. To address this, we propose a new machine learning framework that adapts to both neural signals and emotional cues during language learning. It consists of two main components: the context-aware relational encoder (CARE) and the adaptive contextual enhancement (ACE) strategy. The CARE module represents sentence structure and meaning using attention mechanisms and semantic embeddings. ACE complements this by adjusting learning content in real time using data augmentation, meta-learning, and reinforcement feedback. Together, these components help the system adapt to individual learning needs and emotional responses, improving both engagement and learning outcomes. Our experiments show that this approach improves language understanding and adaptability across different emotional and linguistic scenarios, outperforming existing models on standard benchmarks. These results show the value of combining brain and emotional data to build smarter and more personalized language learning tools for education.