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
arXiv:2610.23149v1 Announce Type: cross
Abstract: Large language model chatbots have been proposed as AI-interviewers for collecting qualitative interview data at scale, potentially bridging social s...
By Semra Guven, Tobias G{\aa}rdhus, Andreas Bjerre-Nielsen, Anna Rogers, Hjalmar Bang Carlsen
arXiv:2607. 01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation.
By Yeji Park, Jiwon Tark, Taesik Gong
arXiv:2608.22266v1 Announce Type: new
Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A c...
By Zhihong Cao, Chen Huang
Preparing for job interviews is important for securing desired positions, yet realistic practice remains difficult to access: real interviews are infrequent, expert mock coaching is costly, and self-practice offers neither adaptive dialogue nor structured assessment. Existing systems typically address only parts of this need through fixed question sequences, limited communication channels, or feedback with little supporting evidence.
The study examines whether a one‑hour adaptive interview can reveal a person’s value‑laden decision policies to a large language model (LLM). Forty‑six adults underwent the interview and later answered 25 dilemma questions; the researchers compared predictions from various transcript segments, adaptive versus fixed follow‑ups, and a baseline model with no personal data. Results show that no part of the interview reliably predicts the participants’ answers better than the baseline, and adaptive follow‑ups offer no advantage over fixed ones, though the transcripts do help recover the participants’ MBTI types.
By Ruoxi Su, Yuhan Liu, Jingyu Hu
The paper introduces a prompt‑engineering framework that personalizes large language model (LLM) teaching assistants across disciplines by tailoring responses to six learner‑specific dimensions, creating 96 distinct learner profiles. It also analyzes student queries through Bloom’s Taxonomy to gauge cognitive complexity, encoding both learner attributes and cognitive assessments into structured prompts that condition the LLM without retraining. Experiments using NLP metrics and a small human study demonstrate that this approach yields perceptible differences in response style and structure, with statistical evidence linking specific learner attributes to measurable changes.
By Saptarshi Basu, Sandeep Kakar, Ashok Goel
arXiv:2608.31007v1 Announce Type: cross
Abstract: Understanding how psychiatric patients subjectively experienced a clinical conversation is important for feedback and alliance-related process monito...
By Aowen Shi, Michal Balazia, Danilo Postin, Ren\'e Hurlemann, Jan Alexandersson, Fran\c{c}ois Br\'emond, Philipp M\"uller
arXiv:2606. 00809v1 Announce Type: new Abstract: Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person.
By Yimin Shi, Clarice Wang, Haixun Wang, Xiaokui Xiao
arXiv:2608. 01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts.
By B. Sankar, Pawni Yadav, Srinidhi Ranjini Girish, Amogh A. S
PRAGMA is a benchmark designed to evaluate personalized guidance in long‑term conversations. It includes curated longitudinal conversation histories, evidence annotations, and guidance scenarios that reflect evolving user contexts and incorrect assumptions. Experiments show that current retrieval, memory, and long‑context models struggle to recover relevant conversational evidence and to use it effectively for personalized guidance.
By Hyojeong Yu, Hyukhun Koh, Minsung Kim, Yunah Jang, Kyomin Jung
arXiv:2603. 09995v2 Announce Type: replace-cross Abstract: Behavioral interview evaluation using large language models presents unique challenges that require structured assessment, realistic interviewer behavior simulation, and pedagogical value for candidate training.
By Kewen Zhu, Zixi Liu, Yanjing Li, Jing Chen