Envisioning AI Support during Semi-Structured Interviews Across the Expertise Spectrum

Abstract

Semi-structured interviews are a critical qualitative method in many areas, including CSCW and HCI, enabling researchers to uncover deep contextualized insights. Using this flexible method, interviewers must adapt to interviewees' responses while adhering to the protocol, necessitating strong active listening and real-time analytical skills. While recent studies have explored how AI can support researchers in qualitative analysis, to our knowledge, no research has investigated AI's role in supporting semi-structured interviews while they are underway. Taking one step toward filling this gap, we interviewed 16 researchers with a range of prior interviewing expertise. Our inductive thematic analysis reveals that interviewers expect real-time AI assistance to support research objectives and facilitate interpersonal communication, but also have concerns about its impact on long-term skill development. We discuss how semi-structured interviews differ from other problem-solving or creative human-AI collaboration contexts, highlighting the time constraints, multimodal collaboration, and the triangular dynamic among interviewers, interviewees, and AI. We also delve into how interviewers' levels of expertise affect their envisioned interviewer-AI collaboration. We then propose design challenges for future CSCW work on AI-driven assistants in interview contexts.

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