2025/06/26 by Mustafa Demir, Jacob Miratsky, Demir, Mustafa +9
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Computational Engineering #Computers and Society (cs.CY) #Education, Achievement, and Giftedness #FOS: Computer and information sciences #Finance #Human-Computer Interaction (cs.HC) #Intelligent Tutoring Systems and Adaptive Learning #Psychological and Educational Research Studies #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2506.22520
openalex publication_date 2025/06/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study examines the impact of an Artificial Intelligence tutor teammate (AI) on student curiosity-driven engagement and learning effectiveness during Interactive Molecular Dynamics (IMD) tasks on the Visual Molecular Dynamics platform. It explores the role of the AI's curiosity-triggering and response behaviors in stimulating and sustaining student curiosity, affecting the frequency and complexity of student-initiated questions. The study further assesses how AI interventions shape student engagement, foster discovery curiosity, and enhance team performance within the IMD learning environment. Using a Wizard-of-Oz paradigm, a human experimenter dynamically adjusts the AI tutor teammate's behavior through a large language model. By employing a mixed-methods exploratory design, a total of 11 high school students participated in four IMD tasks that involved molecular visualization and calculations, which increased in complexity over a 60-minute period. Team performance was evaluated through real-time observation and recordings, whereas team communication was measured by question complexity and AI's curiosity-triggering and response behaviors. Cross Recurrence Quantification Analysis (CRQA) metrics reflected structural alignment in coordination and were linked to communication behaviors. High-performing teams exhibited superior task completion, deeper understanding, and increased engagement. Advanced questions were associated with AI curiosity-triggering, indicating heightened engagement and cognitive complexity. CRQA metrics highlighted dynamic synchronization in student-AI interactions, emphasizing structured yet adaptive engagement to promote curiosity. These proof-of-concept findings suggest that the AI's dual role as a teammate and educator indicates its capacity to provide adaptive feedback, sustaining engagement and epistemic curiosity.