Cosentino et al. (2025) explore the role of generative AI (GenAI) in providing formative feedback within embodied mathematics learning environments. Building on embodied cognition theory and advances in multimodal learning technologies, the study examines whether AI-generated feedback can effectively support students’ learning processes compared to traditional teacher feedback. The research focuses on children learning integer operations through a body-scale digital number line, where physical movement is integrated into mathematical reasoning.
Using a between-group experimental design, 34 students aged 11-13 were assigned to either a GenAI feedback condition or a human teacher feedback condition. Students interacted with a multisensory learning environment (MOVES), where their movements were tracked and used to generate real-time, adaptive feedback through a GPT-4–based system. Multimodal data, including eye-tracking, system logs, and behavioral measures, were collected to assess task performance, cognitive load, and information processing patterns.
Results show no significant differences in task-based learning performance between the GenAI and teacher feedback conditions. However, students receiving GenAI feedback demonstrated significantly lower cognitive load and more balanced information processing strategies, as indicated by eye-tracking metrics such as pupil dilation and the Information Processing Index (IPI). In contrast, students in the teacher feedback condition exhibited higher cognitive load and more frequent attention shifts toward irrelevant or incorrect options, suggesting less efficient processing.
Overall, the findings highlight the potential of GenAI as an effective tool for delivering structured, adaptive feedback that enhances learning efficiency without compromising learning outcomes. Rather than replacing teachers, the study emphasizes the value of hybrid intelligence approaches that integrate AI and human feedback to optimize learning experiences. The results provide important implications for designing AI-enhanced, multimodal learning environments that support cognitive engagement and personalized learning in mathematics education.
Source (Open Access): Cosentino, G., Anton, J., Sharma, K., Gelsomini, M., Giannakos, M., & Abrahamson, D. (2025). Generative AI and multimodal data for educational feedback: Insights from embodied math learning. British Journal of Educational Technology, 56(5), 1686-1709.