(WIP) Bridging the Industry-Academia Gap: Assessing the Need for AI Tools in Technical Interview Preparation and Workforce Readiness

Abstract

This research-to-practice Work-in-Progress (WIP) paper explores how students engage with Artificial Intelligence (AI) tools, particularly large language models (LLMs), for technical interview preparation and workforce readiness. Despite the increasing availability of AI-driven career platforms, students report limited familiarity with and use of these tools for interview practice. Grounded in AI literacy and Social Cognitive Career Theory (SCCT), this study examines underuse through the lenses of self-efficacy and outcome expectations. Prior research highlights a persistent Industry-Academia Gap (IAG), where graduates often lack career preparedness due to insufficient workforce-aligned training. While LLMs are widely adopted in professional settings, students frequently overlook their potential for career development. Using a mixed-methods approach, we surveyed students on their use of AI tools for resume building, interview practice, and skill-gap identification. Participants reported which tools they had used, including Big Interview, Interview Warmup, ChatGPT, Bard, and Microsoft Copilot. Our findings revealed minimal adoption of AI tools for career preparation, with higher engagement reported for academic support. These patterns suggest that while students recognize AI's potential, they underutilize it for professional growth. Our findings also highlight the need to embed AI-supported career readiness into computing curricula and to strengthen collaboration between academia and industry. Future work will focus on designing and evaluating targeted AI interventions to enhance self-efficacy, increase perceived utility, and bridge the Industry-Academia Gap, ultimately improving students' readiness for technical interviews and early career success.

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