Recognizing and responding to students’ emotional states during classes is crucial for optimizing learning outcomes, yet it remains challenging for educators. This study presents the development and implementation of a real-time emotion recognition system using convolutional neural networks (CNNs) to analyze students’ facial expressions during in-person classes. Trained on the FER2013 dataset, the system classifies seven distinct emotions with 85% accuracy. An experiment with 20 university students aged 18–25 compared emotional responses across three teaching methodologies: collective, group, and experiential. Experiential teaching elicited the most positive emotions, with 50% of expressions classified as happiness, while collective teaching generated more negative responses. Statistical analysis revealed a significant positive correlation between happiness and academic performance (r = 0.65, p < 0.01) and a negative correlation between fear and performance (r = −0.54, p < 0.05). The system provides educators with quantitative emotional data, enabling real-time adaptation of teaching strategies and retrospective analysis of class dynamics. This research contributes to AI-enhanced education, offering insights into creating more responsive and student-centered learning environments while addressing privacy and data protection considerations throughout the study.

Augmented reality is an educational technology mainly used in disciplines with a strong physical component, such as architecture or engineering. However, its application is much less common in more abstract fields, such as programming and algorithms. Some augmented reality apps for algorithm education exist, but their effect remains insufficiently assessed. In particular, emotions are an important factor for learning, and the emotional impact of augmented reality should be determined. This article inquires about the impact on students’ emotions of an augmented reality tool for learning Dijkstra’s algorithm. This investigation uses an artificial intelligence tool that detects emotions in real time through facial recognition. The data captured with this tool show that students’ positive emotions increased significantly, statistically surpassing negative emotions, and that some negative emotions, such as fear, were considerably reduced. The results show the same trend as those obtained with psychometric questionnaires, but both positive and negative emotions registered with questionnaires were significantly greater than those registered with the artificial intelligence tool. The contribution of this article is twofold. Firstly, it reinforces previous findings on the positive emotional impact of augmented reality on students. Secondly, it shows an alignment of different instruments to measure emotions, but to varying degrees.