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arXiv AI By Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye, Seppo Virtanen, Mohammad Tahir

Evidence-Traceable Dynamic Interviewer Architecture for Expertise-Adaptive Qualitative Interviews Using Local LLMs

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The paper introduces an Evidence-Traceable Dynamic Interviewer Architecture that uses a locally hosted Large Language Model to adapt interview questions in real time based on participants’ expertise. It features a five‑module prompt‑driven design that profiles expertise, generates iterative questions, and maintains conversational continuity. In a study with 246 participants, the system achieved 78.9% agreement with self‑reported expertise and high ratings for relevance, engagement, and satisfaction.

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Evidence-Traceable Dynamic Interviewer Architecture for Expertise-Adaptive Qualitative Interviews Using Local LLMs

The paper introduces an Evidence-Traceable Dynamic Interviewer Architecture that uses a locally hosted Large Language Model to adapt interview questions in real time based on participants’ expertise. It features a five‑module prompt‑driven design, including an Expertise Profiling module that achieved 78.9% exact agreement with self‑reported expertise and a Generate Iterative Questions module that showed a strong expertise‑complexity association. In a study with 246 participants, the system received high ratings for relevance, engagement, and satisfaction, indicating that its adaptive components functioned as intended.

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