The Black Box in the Research Room: LLM Interpretability Challenges in Virtual World Research Methodology

Abstract

The deployment of large language models (LLMs) as autonomous research agents within virtual world studies represents a paradigm shift in social science methodology — and an underexamined interpretability crisis. As immersive research platforms mature from early Second Life deployments to contemporary multi-platform ecosystems spanning VRChat, Roblox, and Meta Horizon, methodologists have developed sophisticated agentic infrastructures in which LLMs function as Primary Agents conducting interviews, Critique Agents monitoring researcher bias, Safety Agents protecting participant wellbeing, and Synthetic Participants pre-testing experimental environments. These roles are not incidental to the research process: they mediate data collection, shape participant experience, and increasingly underwrite validity claims. Yet the interpretability of LLM behaviour in these contexts remains almost entirely unaddressed in the literature. Drawing on a research trajectory spanning Guillet and Penfold's (2013) foundational avatar-based hospitality study — the first known hospitality research conducted exclusively in a virtual world — through to Penfold's (2026) Sequential Process Model for Immersive Inquiry, this paper identifies five distinct interpretability gaps that emerge when LLMs are embedded in virtual world research protocols. We argue that these gaps constitute a genuine validity threat, articulate a structured research agenda for LLM interpretability in immersive research contexts, and call for collaboration between interpretability researchers and virtual world methodologists before agentic research infrastructures scale further. Key words: LLM interpretability, virtual world research, agentic AI, synthetic participants, immersive methodology, research validity

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