Nervousness is a key emotional factor affecting student performance in high-stakes evaluations. Excessive anxiety before or during exams can impair concentration, reduce problem-solving efficiency, and compromise outcomes. Measuring students’ nervousness is therefore essential for fair and effective assessment. This work proposes a neural network-based framework to estimate and monitor nervousness in pre-exam scenarios. A convolutional neural network (CNN), trained for facial emotion recognition, analyzed real-time video streams, classifying seven emotions: anger, disgust, fear, happiness, neutrality, sadness, and surprise. Each emotion was translated into quantitative nervousness scores using a weighted scoring model, allowing continuous tracking of emotional tension during question answering.

The CNN achieved around 70% accuracy in training, ensuring reliable emotion detection and nervousness estimation. Nervousness scores ranged from 0 to 100, derived from weighted associations of emotions such as fear, anger, and sadness, based on psychology and education studies highlighting their impact on learning and test performance. The system produces per-question and overall assessments, culminating in a readiness report indicating if a student is in an adequate emotional state to proceed. By combining emotion recognition with an interpretable scoring model, the framework provides educators with a practical tool to monitor emotional readiness and identify students at risk of underperforming due to anxiety. Preliminary findings show the approach effectively captures variations in nervousness and offers insights into learners’ emotional states. This research contributes to affective computing in education, demonstrating the potential of neural networks to enhance fairness, well-being, and adaptability in assessment environments.