AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses

Runze Cai, Nuwan Janaka, Daniel (Danny) Hyeongcheol Kim, Yang Chen, Shengdong Zhao, Yun Huang, David Hsu
April 2025
Cite DOI
AiGet pipeline: LLM request trigger, context analysis, knowledge generation and selection, output transformation, and follow-up user action

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.

Type
1
Publication
In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI 2025)
Smart-Glasses Proactive AI Informal Learning Human-AI
Daniel (Danny) Hyeongcheol Kim
Daniel (Danny) Hyeongcheol Kim
Human-Centered AI
Researcher & Builder

HCAI researcher and builder, Postdoctoral Research Fellow at KAIST and co-founder of KEONIX Labs.