Smell with Genji: Rediscovering Human Perception through an Olfactory Game with AI
Awu Chen, Vera Yu Wu, Yunge Wen, Yaluo Wang, Jiaxuan Olivia Yin, Yichen Wang, Qian Xiang, Richard Zhang, Paul Pu Liang, Hiroshi Ishii
TL;DR
Olfactory perception is highly subjective and difficult to articulate, yet cultural practices like Genji-ko offer a structured mode of shared scent interpretation. The paper presents Smell with Genji, a system that fuses time-series olfactory sensing, a Transformer-based scent classifier, and an LLM-driven co smelling partner within a Genji-ko inspired game, accompanied by Genji-mon visualizations to ground dialogue. The key contributions are a collaborative human AI olfactory experience that generates real-time pattern representations and aggregates prior interpretations to support reflection. This work demonstrates the potential of sensing-enabled AI to augment human sensory experience in HCI and open pathways for AI mediated sensory interaction and well being.
Abstract
Olfaction plays an important role in human perception, yet its subjective and ephemeral nature makes it difficult to articulate, compare, and share across individuals. Traditional practices like the Japanese incense game Genji-ko offer one way to structure olfactory experience through shared interpretation. In this work, we present Smell with Genji, an AI-mediated olfactory interaction system that reinterprets Genji-ko as a collaborative human-AI sensory experience. By integrating a game setup, a mobile application, and an LLM-powered co-smelling partner equipped with olfactory sensing and LLM-based conversation, the system invites participants to compare scents and construct Genji-mon patterns, fostering reflection through a dialogue that highlights the alignment and discrepancies between human and machine perception. This work illustrates how sensing-enabled AI can participate in olfactory experience alongside users, pointing toward new possibilities for AI-supported sensory interaction and reflection in HCI.