Problem
Research questions and friction points this paper is trying to address.
generative AI
software engineering education
industry-academia gap
curriculum relevance
job readiness
"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI
🤖 AI Summary
The rapid advancement of generative artificial intelligence has exacerbated the misalignment between software engineering education in higher education and industry demands. This study systematically identifies the competency framework required of software engineers in the era of generative AI through a survey of 51 practitioners and 11 in-depth interviews. It reveals, for the first time, an integrative pathway that combines soft skills, traditional engineering competencies, and emerging capabilities such as prompt engineering and output evaluation. Grounded in empirical findings, the work proposes actionable recommendations for curriculum integration and assessment redesign, offering both theoretical grounding and practical guidance for universities to cultivate talent aligned with contemporary software development practices.
📝 Abstract
Although tension between university curricula and industry expectations has existed in some form for decades, the rapid integration of generative AI (GenAI) tools into software development has recently widened the gap between the two domains. To better understand this disconnect, we surveyed 51 industry practitioners (software developers, technical leads, upper management, \etc) and conducted 11 follow-up interviews focused on hiring practices, required job skills, perceived shortcomings in university curricula, and views on how university learning outcomes can be improved. Our results suggest that GenAI creates demand for new skills (\eg prompting and output evaluation), while strengthening the importance of soft-skills (\eg problem solving and critical thinking) and traditional competencies (\eg architecture design and debugging). We synthesize these findings into actionable recommendations for academia (\eg how to incorporate GenAI into curricula and evaluation redesign). Our work offers empirical guidance to help educators prepare students for modern software engineering environments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
software engineering education
prompting
curriculum redesign
industry-academia gap
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