# "Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI

arXiv: https://arxiv.org/abs/2604.06342 · HTML: https://mlsanity.com/paper/2604.06342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education](https://mlsanity.com/paper/2401.03676/) (2024)
- [Challenges and Paths Towards AI for Software Engineering](https://mlsanity.com/paper/2503.22625/) (2025)
- [Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineers](https://mlsanity.com/paper/2302.13229/) (2023)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [Evolution of Programmers' Trust in Generative AI Programming Assistants](https://mlsanity.com/paper/2509.13253/) (2025)
- [Generative AI and the Transformation of Software Development Practices](https://mlsanity.com/paper/2510.10819/) (2025)
- [Understanding the Skills Gap between Higher Education and Industry in the UK in Artificial Intelligence Sector](https://mlsanity.com/paper/2408.10788/) (2024)
- [The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies](https://mlsanity.com/paper/2409.00875/) (2024)
- [Liberating Logic in the Age of AI: Going Beyond Programming with Computational Thinking](https://mlsanity.com/paper/2511.17696/) (2025)
- [An Empirically Evaluated Checklist for Surveys in Software Engineering](https://mlsanity.com/paper/1901.09850/) (2019)

## Abstract (arXiv)

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.