Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students
This addresses a specific problem for CS students preparing for technical interviews, but it is incremental as it builds on existing conversational AI research with a focus on user perceptions and design.
The study tackled the limited structured practice for think-aloud processes in technical interviews by developing an LLM-based tool, finding that 17 participants valued AI for simulation, feedback, and learning from examples, with key design recommendations identified.
One challenge in technical interviews is the think-aloud process, where candidates verbalize their thought processes while solving coding tasks. Despite its importance, opportunities for structured practice remain limited. Conversational AI offers potential assistance, but limited research explores user perceptions of its role in think-aloud practice. To address this gap, we conducted a study with 17 participants using an LLM-based technical interview practice tool. Participants valued AI's role in simulation, feedback, and learning from generated examples. Key design recommendations include promoting social presence in conversational AI for technical interview simulation, providing feedback beyond verbal content analysis, and enabling crowdsourced think-aloud examples through human-AI collaboration. Beyond feature design, we examined broader considerations, including intersectional challenges and potential strategies to address them, how AI-driven interview preparation could promote equitable learning in computing careers, and the need to rethink AI's role in interview practice by suggesting a research direction that integrates human-AI collaboration.