Date of Award

6-2026

Degree Name

MS in Computer Science

Department/Program

Computer Science

College

College of Engineering

Advisor

Franz Kurfess

Advisor Department

Computer Science

Advisor College

College of Engineering

Abstract

In Search and Rescue (SAR) operations, time pressure and limited interviewer experience can lead to missed opportunities when interviewing a missing person’s friends and family. This thesis presents a real-time, end-to-end system that provides context-aware follow-up question suggestions as interviews unfold. Leveraging large language models (LLMs) and agentic design patterns, the system is intended to support interviewers by helping them identify relevant follow-up questions and pursue potentially overlooked lines of inquiry.

The system was evaluated through three mock interviews with two SAR interviewer participants across two events. Given the limited sample size, the results provide early insights into the feasibility of AI-assisted interviews. Qualitative feedback indicated that many generated questions were relevant and, in some cases, surfaced lines of inquiry they had not previously considered. Quantitative results suggest strong usability, with System Usability Scale (SUS) scores improving from 77.5 to 95 across iterations and participants reporting a high likelihood of recommending the system (9/10 to 10/10).

Overall, this work provides early evidence of the technical feasibility and perceived value of real-time LLM-assisted interviewing tools in mock SAR contexts. The findings suggest that such tools may support more thorough questioning, motivating future work to evaluate their effects on clue discovery, interview completeness, and missed information.

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