Multimodal Emotion Recognition in Educational Environments Using Artificial Intelligence and Forensic Psychology

This study explores how multimodal emotion recognition can be leveraged in educational environments, combining AI methodologies with forensic psychology to improve human-computer interaction and behavioral analysis. The research used a mixed-methods approach, gathering data through facial expression analysis, speech signal processing, and annotated datasets. For the technical side, the team applied advanced machine learning algorithms—specifically, support vector machines and deep learning architectures—for classifying emotional states.The multimodal fusion model, which incorporates both facial and speech data, achieved 91% accuracy, outperforming any single-modality method. This really highlights the advantage of integrating multiple sources of information.These findings suggest that multimodal systems could play a significant role in real-time educational assessment and behavioral prediction, especially in multilingual settings. By bringing forensic psychology into the mix, the system gains a more nuanced perspective on student emotions—making it a valuable tool for adaptive learning platforms and early intervention strategies.