Adaptive Artificial Intelligence to Teach Interactive Molecular Dynamics in the Context of Human-Computer Interaction

14 pages•Published: July 12, 2024

Mustafa Demir, Sean M. Leahy, Punya Mishra, Chun Kit Chan and Abhishek Singharoy

Abstract

Artificial Intelligence (AI) can be easily integrated into virtual education to drive adaptive instruction and real-time constructive feedback to students, offering a possible conduit for fostering discovery curiosity in learners. This study examines and characterizes Human-AI-Teaming (HAT) coordination dynamics to monitor the inception of discovery curiosity in online laboratories of interactive molecular dynamics (IMD). We used molecular physics measures (kinetic/ potential energy and action) obtained from simple and complex examples of simulated mouse tracking datasets in IMD log files as a proxy for understanding the context of molecular sciences and developing novel interactions for inquiry. These measures are good features of our HAT context because kinetic energy reflects the system’s atoms’ overall motion regarding the individual atoms’ speed. While kinetic energy represents if a learner applies artificial forces to the task, potential energy can be AI’s response to these forces. The action is a systems-level reaction to the changes during the task. By applying nonlinear dynamical systems methods to the physics measures, we extracted the Largest Lyapunov Exponent and Determinism metrics as HATs’ coordination stability and predictability, respectively. The findings underline that while the more complex IMD task required less stable and predictable HAT coordination dynamics, the simple task is more. One explanation is that AI needs to anticipate the learner by providing feedback at the right time and place during the more complex IMD task to initiate and sustain the learner's discovery curiosity. In IMD, future HAT design should consider coordination dynamics for fostering ‘discovery curiosity’ and practical learning.

Keyphrases: artificial intelligence, curiosity, human computer interaction, human machine teaming, molecular dynamics, nonlinear dynamical systems

In: Kenneth Baclawski, Michael Kozak, Kirstie Bellman, Giuseppe D'Aniello, Alicia Ruvinsky and Candida Da Silva Ferreira Barreto (editors). Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023, vol 102, pages 140-153.

Links:	https://easychair.org/publications/paper/x4Pg
	https://doi.org/10.29007/vclz

BibTeX entry

@inproceedings{CogSIMA2023:Adaptive_Artificial_Intelligence_Teach,
  author    = {Mustafa Demir and Sean M. Leahy and Punya Mishra and Chun Kit Chan and Abhishek Singharoy},
  title     = {Adaptive Artificial Intelligence to Teach Interactive Molecular Dynamics in the Context of Human-Computer Interaction},
  booktitle = {Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023},
  editor    = {Kenneth Baclawski and Michael Kozak and Kirstie Bellman and Giuseppe D'Aniello and Alicia Ruvinsky and Candida Da Silva Ferreira Barreto},
  series    = {EPiC Series in Computing},
  volume    = {102},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/x4Pg},
  doi       = {10.29007/vclz},
  pages     = {140-153},
  year      = {2024}}

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