This work investigated how interviewers experience AI assistance for probing during semi-structured interviews and proposed three implications for AI-assisted human-to-human interaction: managing social pressure, balancing idea alignment with inspiration, and preserving interpersonal presence.
Abstract
Eliciting rich data in semi-structured interviews is cognitively demanding, prompting recent work to explore real-time AI assistance for interviewers. However, introducing AI into the interviewer-interviewee interaction creates a triadic context whose social dynamics remain underexplored. We investigated how interviewers experience AI assistance for probing during semi-structured interviews. To elicit rich participant reflections, we implemented two variants of AI assistance differing in initiation and granularity in a high-fidelity prototype, ProbeAssist. We conducted a qualitative-first comparative structured observation study where 18 participants each completed three simulated interviews: one without AI and two with different AI variants. Findings showed that participants leveraged AI as a supportive tool but resisted it as an assessor or competitor. As they navigated AI's benefits and interaction costs, tensions emerged around agency, ownership, creativity, and interpersonal communication. We propose three implications for AI-assisted human-to-human interaction: managing social pressure, balancing idea alignment with inspiration, and preserving interpersonal presence.
As large language models are increasingly used in evaluative settings for their conversational potential in interviews, systematic frameworks for assessing their methodological quality are still lacking. Drawing on evaluation, qualitative interviewing, and validity theories, this study develops a theory-grounded framew...
Ali Safarnejad, Hippolyte Lefebvre· American Journal of Evaluati...· 0 citations
Semi-structured interviews are a cornerstone of qualitative research but remain labor-intensive. We report an empirical study of what actually happens when the interviewer is an off-the-shelf real-time multimodal LLM (MLLM). We built InterviewBot, a voice-based interviewing system that wraps a real-time MLLM with a res...
Zhang He, Kambinachi Chukwuma, Chanmin Kim et al.· Proceedings of the 2026 ACM...· 0 citations
A growing body of work uses AI-based simulation to teach communication skills, but current simulation-based communication training (SBCT) systems make inconsistent choices on design dimensions that prior theory identifies as critical: how skills are modeled, when feedback is given, and how self-reflection is scaffolded...
Jessica R. Mindel, Si-Jia Xie, Yee Chan et al.· Proceedings of the 2026 ACM...· 0 citations
The Reflective Conversational Journal operationalizes a complementary human–AI role structure in which AI supports facilitation and recording while the user retains the reflector role, providing preliminary evidence of design validity and implementation fidelity rather than objective evidence of cognitive-load reductio...
Past‐behavior questions prompt interviewees to recount past work experiences as stories. Telling a good story, however, typically relies on feedback from the audience. In asynchronous video interviews (AVIs), which lack real‐time interaction, interviewees often produce
extended responses
, continuing to speak aft...
K. Orji, Adrian Bangerter, Elisabeth Germanier et al.· International Journal of Sel...· 0 citations
People increasingly use general-purpose chatbots such as ChatGPT, Claude, and Gemini for mental health and emotional support. We report a multi-stage longitudinal qualitative study of 18 U.S. adults, conducted from April to December 2025, combining initial interviews, a four-week diary study, focus groups, and exit int...
Meryl Ye, Briana Vecchione, Livia Garofalo et al.· 0 citations
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