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Deciphering User Gaze Dynamics: Interacting an AI-Driven Platform with a Chatbot for Problem Solving

In: Information Systems and Neuroscience

Author

Listed:
  • Jonathan Fu

    (Chapin High School)

Abstract

In this study, we investigate user interactions on an Artificial Intelligence (AI)-enabled platform, featuring a chatbot designed to augment user engagement in learning how to solve a puzzle. Our platform’s core AI algorithm leverages macro-actions-sequences of moves, not interpretable or usable to users, essentially functioning as a ‘black box.’ To address this, we employed scaffolding design strategies and explainable AI principles to develop an innovative conversational user interface (UI). Utilizing eye-tracking techniques, we collected users’ gaze data to assess their gaze patterns and attention distribution during a problem-solving process. By focusing on gaze metrics, e.g., fixation duration, saccade frequency, area of interest, and heatmap, we found users’ visual attention correlates with scaffolding-enabled UI elements, and thus positively influencing their problem-solving experience. This preliminary study allows us to evaluate the UI's intuitiveness and identify design strategies for user engagement improvement, adaptable to diverse learning styles, and thus potentially enhancing problem-solving efficiency on our AI platform.

Suggested Citation

  • Jonathan Fu, 2025. "Deciphering User Gaze Dynamics: Interacting an AI-Driven Platform with a Chatbot for Problem Solving," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 21-27, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-71385-9_3
    DOI: 10.1007/978-3-031-71385-9_3
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