Machine Eye: Designing Relational Engagement with Embodied Large Language Models

Large Language Models (LLMs) are increasingly being integrated into embodied, tangible, and ambient forms, expanding beyond the established chatbot interaction paradigm. LLMs are inherently general and open-ended. In contrast, design practice typically stabilises artefacts by prescribing their role or function through fixed metaphors. We present Machine Eye, a Research through Design (RtD) exploration of an embodied LLM that resists metaphorical closure. Rather than prescribing a specific role or function, the artefact is deliberately ambiguous, inviting participants to explore new forms of relational engagement with AI. Firstly, we explicate our design process, revealing three key tensions encountered when designing against metaphor for embodied LLMs. Secondly, we present findings from a qualitative study (N=15) investigating how participants interpret and engage with Machine Eye. We find that as participants actively explore new and non-prescriptive modes of embodied interaction, perceived roles can be dynamically contested and renegotiated, allowing for a kind of boundless relationship to emerge.