## 2 Related Work
### Assessment of Emotions  
The current literature includes several methods and instruments for assessing the impact of technology on emotions in the educational setting. Psychometric questionnaires are probably the most common tool, where students typically rate different questions or statements on how they feel about the learning experience. For example, the AEQ [12] and PANAS (Positive and Negative Affect Schedule) [13] questionnaires assess the positive and negative emotions experienced during learning in this way. Other questionnaires used are DASS (Depression Anxiety Stress Scale) and POMS (Profile of Mood States) [14], which measure student tension, fatigue, anxiety, and stress. Complementary instruments include the CD-RISC (Connor-Davidson Resilience Scale) [15], which evaluates emotional resilience, analyzing the student's ability to face challenging situations in the educational environment. Psychometric questionnaires have limitations due to participant subjectivity and the difficulty in capturing emotions in real time. For example, students may not fully understand the questions posed and, therefore, may answer inaccurately. Or, if questionnaires are completed after the learning experience, students respond based on what they remember, which can lead to a discrepancy between the feelings they experienced and what they expressed [16; 17]. These limitations motivate the need to explore complementary assessment methods that offer a more complete and accurate view of students' emotions. Computer technology makes it possible to measure the emotions experienced while learning more objectively and continuously [18]. Biometric methods, such as physiological sensors and electroencephalography (EEG) devices, make it possible to capture emotions in real time in an objective manner, providing more detailed and accurate data than that obtained through traditional questionnaires [19]. Physiological sensors make it possible to monitor emotions in real time to analyze emotional responses in educational settings [17]. Electroencephalography has been used to study brain activity linked to emotions. It has proven effective in research that analyzes how different technological resources influence the emotional state of students [20]. It is also applied in studies that relate physical activity with emotions through Frontal Alpha Asymmetry (FAA) [21]. Recent advances have used neural networks to improve the accuracy of emotion recognition through EEG signals [22]. However, biometric instruments have the problem of their invasive nature, as they force the student to wear sensors and devices with limited usability that prevent free interaction [19]. AI combined with non-invasive devices is a recent line of research that attempts to solve this problem, for example, through facial emotion recognition [18]. Examples of these systems are found in work environments and e-learning environments [23]. Some systems use traditional algorithms for facial recognition. Algorithms such as Eigenfaces [24] and Viola-Jones [25] have been successfully applied in emotion detection, achieving high accuracy in educational environments. Their use has shown that their combination with deep learning models allows for accurate facial recognition in real time [8]. These technologies make it possible to generate detailed reports on students' emotional behavior, enabling adaptive education [26]. Emotions have also been identified in online learning using models based on convolutional neural networks (CNNs), providing valuable feedback regarding the emotional impact on academic performance [27]. However, these systems have limitationsin terms of scalability, accuracy, or applicability in face-to-face educational environments. There are some other techniques, although they are less commonly used. Techniques such as text analysis using natural language processing (NLP) have made it possible to infer emotions from student writing, such as essays or forum discussions [27].