AI-Driven Emotion-Aware Learning with Cross-Modal Fusion

This study proposes a novel approach to emotion-aware adaptive learning by combining Cross Model Attention mechanisms with a Hybrid Artificial Intelligence (AI) framework that combines Deep Reinforcement Learning (DRL) with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The goal is to develop a customized, emotionally intelligent learning environment that dynamically modifies course material according to students’ emotional and cognitive conditions. To infer emotional states in real time, the system records multimodal data, including text inputs, voice tones, and facial expressions. To ensure accurate emotion recognition and context-aware learning responses, a Cross Model Attention module efficiently fuses and prioritizes features from several modalities. The proposed model uses supervised pretraining and reinforcement-based fine-tuning to optimize decision-making policies for personalized content recommendation. It is used in a simulated e-learning environment where feedback loops modify the pace, interaction style, and complexity of the content according to learner emotions and performance indicators. According to experimental findings, the paradigm considerably improves learner satisfaction, engagement, and retention of information. Additionally, the system’s modular and scalable design enables interaction with current learning management systems (LMS). Data preparation pipelines, latency control for real-time emotion recognition, and ethical issues with emotional profiling and privacy are all covered in the study. The results demonstrate how hybrid AI-driven systems have the potential to transform adaptive education by establishing learner-cantered, emotionally intelligent environments. For real-time emotion-aware adaptive learning, this work introduces a unique architecture that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Deep Reinforcement Learning (DRL), and Cross-Modal Attention. In contrast to current methods, our hybrid system uses multimodal inputs to dynamically adapt material delivery based on learners’ emotional and cognitive states.