Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students

TL;DR

This paper explores how conversational AI can support CS students in practicing think-aloud techniques for technical interviews, emphasizing user perceptions, design recommendations, and equitable learning strategies.

Contribution

It introduces design principles for AI-driven interview practice tools, highlighting social presence, feedback mechanisms, and human-AI collaboration for equitable learning.

Findings

Participants valued AI for simulation and feedback

Design recommendations include promoting social presence

Highlights importance of human-AI collaboration for equity

Abstract

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.