arXiv Computation and Language
Can AI interview? Comparing AI-led to Human-led Chat Interviews
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InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers
InterviewPlayground is a simulation environment designed to evaluate AI interviewers by using simulated participants whose behaviors are grounded in social theory. The platform generates an InterviewReportCard that assesses AI interviewers across validated measures. Empirical validation shows that performance in InterviewPlayground predicts human study outcomes with a Pearson correlation of 0.86, supporting its use for assessing AI interviewer performance and failure modes.
arXiv AI
Oct 9
InterviewPlayground: A Simulation Environment for Evaluating AI Interviewers
InterviewPlayground is a simulation environment designed to evaluate AI interviewers by using simulated participants whose behaviors are grounded in social theory. The platform generates an InterviewReportCard that assesses AI interviewers across validated measures. Empirical validation shows a strong correlation (average Pearson 0.86) between simulation results and real human studies, confirming the environment’s predictive validity.
arXiv AI
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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 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.
Hugging Face Trending Papers
Oct 8
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.
arXiv AI
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Hybrid Panels: Toward Human-AI Collaboration in Survey Research
arXiv:2608.22582v1 Announce Type: cross Abstract: Large-scale population surveys are essential for generating robust social and scientific insights, yet they face significant challenges, including de...
arXiv AI
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HARP: The Human--AI Research Platform
arXiv:2607. 20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges.