INTELLIGENT EMOTION-AWARE LEARNING: A MULTIMODAL FRAMEWORK FOR HUMAN-CENTRIC EDUCATION

Abstract

The rapid advancement of digital learning technologies has transformed educational environments, creating opportunities for more personalized and adaptive learning experiences. However, conventional smart learning systems primarily focus on academic performance and content delivery while often overlooking the emotional and cognitive states of learners. Since emotions significantly influence attention, motivation, engagement, and knowledge retention, integrating emotional intelligence into educational platforms has become essential for developing effective human-centric learning environments. This study proposes an Intelligent Emotion-Aware Learning Framework (IEALF) that leverages multimodal emotion recognition techniques to create adaptive and personalized educational experiences. The framework utilizes multiple data modalities, including facial expressions, speech characteristics, physiological signals, and behavioral interactions, to accurately identify learners emotional states in real time. The proposed framework employs advanced machine learning and deep learning models for multimodal data fusion and emotion classification. A hybrid architecture combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and attention-based fusion mechanisms is used to extract emotional patterns from diverse input sources. Based on the detected emotional states, the system dynamically adapts learning content, instructional strategies, feedback mechanisms, and difficulty levels to enhance learner engagement and comprehension. Furthermore, the framework incorporates a human-centric decision layer that ensures personalized interventions while maintaining ethical considerations, privacy protection, and learner well-being. Experimental evaluation was conducted using benchmark multimodal emotion datasets and simulated smart learning environments. The results demonstrate that the proposed framework achieves high emotion recognition accuracy, improved learner engagement, enhanced knowledge retention, and increased learning satisfaction compared with conventional adaptive learning systems. The multimodal approach significantly outperforms single-modality emotion recognition techniques by providing robust and reliable emotional insights. The findings indicate that intelligent emotion-aware learning systems can play a vital role in the future of personalized education, enabling more responsive, empathetic, and effective learning experiences. The proposed framework offers a promising foundation for next-generation humancentric educational technologies and emotionally intelligent smart learning ecosystems.