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This paper introduces a novel approach to generating executable smart home schedules by leveraging large language models (LLMs) to create diverse resident personas that interact with a simulated environment. By developing a design framework that configures households across five socio-technical dimensions and a multi-stage LLM pipeline for structured interaction schedules, the authors address the challenges of dataset collection in real homes, which is often slow, costly, and privacy-invasive. The proof of concept demonstrates the feasibility of this method, paving the way for scalable and privacy-conscious smart home experimentation.
LLMs can generate diverse resident personas that produce executable smart home interaction schedules, eliminating the need for intrusive real-world data collection.
Smart homes have emerged as an important domain for HCI research, including work on usable security and privacy. Ideally, studies in these areas draw on datasets collected in real homes with real residents, capturing authentic device interactions, network traffic, and daily routines. However, creating such datasets is slow, expensive, and raises significant privacy concerns, as it requires long-term observation of people in their most private spaces. We propose using LLMs to generate diverse resident personas that interact with a simulated smart home, producing behaviorally grounded interaction schedules that can be executed on physical testbeds. We present (1) a design framework configuring simulated households across five socio-technical dimensions, (2) a multi-stage LLM pipeline that produces structured, executable device interaction schedules, and (3) a proof of concept demonstrating feasibility. As a work in progress, we aim to support scalable, privacy-conscious smart-home experimentation without relying on intrusive real-world data collection.
