Using Deep Learning and CNNs for Enhancing Student Learning Through AI-Driven Facial Emotion Recognition

Recognising emotions is paramount in assessing students’ engagement and improving their educational performance. This study investigates a deep learning and convolutional neural network (CNN)-based facial emotion recognition system to analyse students’ facial expressions in real time. The model proposed improves classical learning methods by offering the analysis of students' emotional states, which, in turn, facilitates adaptive learning and teaching approaches. The system combines feature extraction by CNNs and classification using deep learning, achieving a high accuracy in recognising several different emotions, including happiness, frustration, and confusion. Findings of this study demonstrate the role of AI-powered emotion recognition in transforming the conventional teaching approach to an interactive and learner-centred paradigm. This research approaches the development of intelligent technologies for education through the integration of AI, psychology, and pedagogy to increase students’ academic engagement and performance.