Multimodal Attention Evaluation in Child–Robot Storytelling: A Machine Learning Framework with NAO

Understanding and measuring children's attention is a key challenge in educational human-robot interaction (HRI). This paper presents a novel evaluation framework for detecting attention in school-age children during a storytelling activity with the NAO social robot. Unlike traditional scenarios where the robot narrates, here the child actively tells a story while the robot listens and adapts, fostering engagement through constructivist and sociocultural learning principles. We integrate multimodal features - including gaze behaviour (automatically labelled using Gaze360 and K-means clustering), task performance, and physiological signals (heart rate) - to classify attention levels via machine learning methods (SVM, KNN, RF). Seventy-four children (aged 7-9) participated in the study, with attention labels validated by expert observers. Results show that combining gaze, task, and physiological features improves classification accuracy (>0.70) compared to unimodal approaches, with KNN achieving the best performance. These findings highlight the potential of multimodal attention detection for enabling adaptive, context-aware robot behaviours in education. Our framework advances the development of socially intelligent robots that can dynamically respond to children's attentional states, ultimately supporting more engaging and effective learning experiences.