用对话AI模拟技术面试中的自言自语，提升学生练习效果。
Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students
- 基于大模型构建对话式面试练习工具，支持思考过程模拟。
- 用户认可AI在反馈与示例生成方面的价值，提升练习效率。
- 适合准备技术面试的计算机专业学生及教育研究者。
技术面试中的自言自语环节要求候选人边解题边表达思路，但系统性练习机会有限。尽管对话式AI具备辅助潜力，但其在该场景下的用户体验研究仍不足。本研究通过17名参与者的实验，评估基于LLM的技术面试练习工具。参与者普遍认为AI在模拟、反馈和学习生成示例方面具有价值。设计建议包括增强AI的社会存在感、提供超越语言内容的多维反馈，以及通过人机协作实现众包思考过程案例。此外，研究还探讨了交叉性挑战、公平学习路径，提出应以人机协同为方向重构AI在面试准备中的角色。
原文摘要 · Abstract (English)
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.
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