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

arXiv: https://arxiv.org/abs/2506.22520 · HTML: https://mlsanity.com/paper/2506.22520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [GPT-3-driven pedagogical agents for training children's curious question-asking skills](https://mlsanity.com/paper/2211.14228/) (2022)
- [Promises and challenges of generative artificial intelligence for human learning](https://mlsanity.com/paper/2408.12143/) (2024)
- [Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package](https://mlsanity.com/paper/1310.0201/) (2013)
- [Conversational agents for fostering curiosity-driven learning in children](https://mlsanity.com/paper/2204.03546/) (2022)
- [Generative AI as a metacognitive agent: A comparative mixed-method study with human participants on ICF-mimicking exam performance](https://mlsanity.com/paper/2405.05285/) (2024)
- [DeBiasMe: De-biasing Human-AI Interactions with Metacognitive AIED (AI in Education) Interventions](https://mlsanity.com/paper/2504.16770/) (2025)

## Abstract (arXiv)

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.