Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics

• URL: http://arxiv.org/abs/2506.22520v1
• Date: Thu, 26 Jun 2025 19:30:25 GMT
• Title: Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
• Authors: Mustafa Demir, Jacob Miratsky, Jonathan Nguyen, Chun Kit Chan, Punya Mishra, Abhishek Singharoy,
• Abstract summary: 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.<n>The study further assesses how AI interventions shape student engagement, foster discovery curiosity, and enhance team performance within the IMD learning environment.
• Score: 1.0484829165073797
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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