<![CDATA[The integration of affective aspects into adaptive learning systems remains limited, as most educational technologies primarily rely on cognitive performance indicators. However, students’ emotional conditions significantly influence engagement, motivation, and learning outcomes. This study aims to develop and evaluate a Convolutional Neural Network (CNN) model for classifying students’ emotions based on facial expressions to support adaptive learning environments. A quantitative experimental approach was employed. Facial expression image data were preprocessed through face detection, resizing, normalization, and data augmentation before being trained using a CNN architecture with the Adam optimizer and categorical cross-entropy loss function. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the proposed CNN model achieved an overall accuracy of 90% with an average F1-score of 0.88 across four emotion categories (Happy, Sad, Neutral, and Angry). The confusion matrix indicates that most predictions fall within the correct classification range, although minor misclassifications occurred between low-intensity Sad and Neutral expressions. The stability of training and validation loss curves demonstrates good generalization ability without significant overfitting. These findings indicate that CNN-based facial emotion classification can serve as a reliable component in adaptive learning systems by providing real-time affective feedback. The study contributes to the development of artificial intelligence applications in education by integrating emotional recognition into adaptive instructional design]]>
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