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

Jonathan Fu ()
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Jonathan Fu: Chapin High School

A chapter in Information Systems and Neuroscience, 2025, pp 21-27 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Human-AI interaction; Human–computer interaction; Explainable AI; Chatbot; Eye tracking; Gaze behaviors; Puzzle; Problem-solving (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-71385-9_3

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DOI: 10.1007/978-3-031-71385-9_3

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