Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908426921050112 |
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| author | Demir, Mustafa Miratsky, Jacob Nguyen, Jonathan Chan, Chun Kit Mishra, Punya Singharoy, Abhishek |
| author_facet | Demir, Mustafa Miratsky, Jacob Nguyen, Jonathan Chan, Chun Kit Mishra, Punya Singharoy, Abhishek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics Demir, Mustafa Miratsky, Jacob Nguyen, Jonathan Chan, Chun Kit Mishra, Punya Singharoy, Abhishek Human-Computer Interaction Artificial Intelligence Computational Engineering, Finance, and Science Computers and Society 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. |
| title | Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics |
| topic | Human-Computer Interaction Artificial Intelligence Computational Engineering, Finance, and Science Computers and Society |
| url | https://arxiv.org/abs/2506.22520 |