Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution
It addresses the problem of improving interview efficiency and quality for qualitative researchers, though it appears incremental as it builds on existing LLM capabilities for a specific domain.
This research tackles the cognitive challenges interviewers face in real-time information processing and question adaptation by developing Interview AI-ssistant, a system for human-AI collaboration in interview preparation and execution, resulting in design guidelines and practical implementations for AI-enhanced qualitative research tools.
Recent advances in large language models (LLMs) offer unprecedented opportunities to enhance human-AI collaboration in qualitative research methods, including interviews. While interviews are highly valued for gathering deep, contextualized insights, interviewers often face significant cognitive challenges, such as real-time information processing, question adaptation, and rapport maintenance. My doctoral research introduces Interview AI-ssistant, a system designed for real-time interviewer-AI collaboration during both the preparation and execution phases. Through four interconnected studies, this research investigates the design of effective human-AI collaboration in interviewing contexts, beginning with a formative study of interviewers' needs, followed by a prototype development study focused on AI-assisted interview preparation, an experimental evaluation of real-time AI assistance during interviews, and a field study deploying the system in a real-world research setting. Beyond informing practical implementations of intelligent interview support systems, this work contributes to the Intelligent User Interfaces (IUI) community by advancing the understanding of human-AI collaborative interfaces in complex social tasks and establishing design guidelines for AI-enhanced qualitative research tools.