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

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