AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses
🤖 AI Summary
This study addresses the challenge of knowledge acquisition conflicting with primary tasks under everyday low-attention conditions. We propose a gaze-driven, proactive informal learning paradigm implemented via AR smart glasses, integrating multimodal sensing (eye tracking + environmental sensors), dynamic user profiling, lightweight LLM-based contextual reasoning, and real-time AR knowledge visualization. The system unobtrusively delivers personalized knowledge during natural activities—such as walking or shopping—without interrupting ongoing tasks. Key contributions include: (1) the first demonstration of zero-interference knowledge discovery during primary-task execution; and (2) novel design principles balancing curiosity stimulation with environmental semantic grounding. Empirical evaluation shows significant improvements in environmental immersion and task enjoyment. Multi-day in-the-wild deployment confirms the approach’s efficacy in uncovering latent interests, rekindling curiosity, and enhancing situated understanding.
Application Category
📝 Abstract
Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet's effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences.
Problem
Research questions and friction points this paper is trying to address.
Incidental Learning
Daily Life
Cognitive Enhancement
Innovation
Methods, ideas, or system contributions that make the work stand out.
AR Glasses
AI Assistant
Personalized Learning
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