Exploring student engagement and learning preferences: A comparative study between virtual- and robot-based tutors

The rapid integration of artificial intelligence (AI) in educational settings has opened new avenues for personalized and interactive learning experiences. This study investigates the comparative effectiveness of virtual- and robot-based tutors in enhancing student engagement and learning preferences. Utilizing advanced AI technologies, we conducted a comprehensive analysis to determine which tutoring modality offers a more effective and engaging learning experience. Through a methodological approach, we collected and analyzed data from participants who interacted with both tutoring systems. Employing k-means clustering and statistical analysis, we identified distinct clusters of learner preferences and engagement levels. Our findings reveal significant differences in the effectiveness of robot- and virtual-based tutors across different learner groups, highlighting the importance of personalized and adaptable AI tutoring systems. The implications of our research suggest a need for further exploration into personalized educational tools for diverse learning styles, thereby optimizing the learning experience in AI-enhanced educational environments.