Supplementary material for the paper “Interviewing Silicon Experts: A Persona-Based LLM Interview Pipeline in Automated Driving”
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Supplementary material for the paper: Fang, Y., Bazilinskyy, P., & Martens, M. (2026). Interviewing Silicon Experts: A Persona-Based LLM Interview Pipeline in Automated Driving. Manuscript submitted to ACM. This study introduces a three-phase, persona-based large language model (LLM) interview pipeline for structuring multidisciplinary expert knowledge before involving human experts. Using drivers' minimum mental model (MMM) for the safe use of automated driving systems as a demonstration topic, the study first constructed 28 expert personas spanning seven disciplines, two technological orientations, and two priorities. Seven differentiated "silicon experts" were then purposively selected, converted into first-person professional backstories, and interviewed independently using a structured protocol. Their JSON-formatted responses were analysed through point-level open coding. The demonstration produced 59 distinct candidate MMM items across six predefined themes. Shared priorities included driver responsibility, concrete operational design domain (ODD) boundaries, and permitted and prohibited non-driving-related tasks (NDRTs), while persona-specific contributions included …
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