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.
By Jonathan Ivey, Aimee Liang, Arthur Y. S. Wang, Madeline Mandell, Ziang Xiao, Anjalie Field
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.
By Jonathan Ivey, Aimee Liang, Arthur Y. S. Wang, Madeline Mandell, Ziang Xiao, Anjalie Field
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.
By Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye, Seppo Virtanen, Mohammad Tahir
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:2608.22582v1 Announce Type: cross
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By Jason Miklian, Kristian Hoelscher, John E. Katsos
This scoping review examined 48 studies on generative AI chatbots designed to deliver motivational interviewing (MI). It found that most systems were text‑based and disembodied, with about half incorporating dynamic adaptation, and that safety reporting was inconsistent. While user perceptions were generally positive and many studies reported MI‑consistent interactions, evidence for sustained behavioral or functional change remains limited.
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arXiv:2607. 08285v1 Announce Type: new Abstract: Current AI evaluation frameworks focus primarily on technical performance, including accuracy, robustness, reasoning ability, and policy compliance.
By Marcos Economides, Paul M. Sacher, Samuel Salzer, Alexis Michelle Abellar, Fendi Tsim, Antoine Ferr\`ere
arXiv:2609.00250v1 Announce Type: cross
Abstract: Many people now see AI systems as not just productivity tools but as social companions. Researchers are eager to study the consequences of AI compani...
By Jacy Reese Anthis, Mark D\'iaz, Renee Shelby
arXiv:2607. 08748v1 Announce Type: new Abstract: In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education.
By Kristina Schaaff, Quintus Stierstorfer, Valerie Heckel
arXiv:2606. 30660v1 Announce Type: cross Abstract: Collecting reliable social data from low-literacy populations remains a persistent challenge, particularly when surveys involve sensitive topics and marginalized communities.
By Raj Gaurav Maurya