Four Years of GenAI: How Educators and Industry Adapted Their Assessment Strategies
2026-07-27 • Software Engineering
Software Engineering
AI summaryⓘ
The authors studied how entry-level software engineering skills are changing because of Generative AI (GenAI). They surveyed educators and hiring professionals to see their views on teaching and assessing these skills. Educators focus on preventing cheating with GenAI, while companies commonly use it and set rules for how to use it. Both groups want tests that show real-time problem-solving and value skills like judging AI output and learning independently, but employers think new graduates aren’t ready enough in some basic skills. There is still debate on whether candidates should be good at using GenAI in job interviews.
Generative AISoftware engineeringEntry-level skillsAcademic integrityHiring assessmentsAI-assisted codingCritical evaluationReal-time interactionsSkill gapsIndustry policy
Authors
May Mahmoud, Nisa Shahid, Izah Sohail, Gulshan Sharma, Hanan Salam, Sarah Nadi
Abstract
GenAI's ability to solve a wide range of software engineering tasks is reshaping the software industry, raising the question of what an entry-level software engineer looks like today. In this work, we investigate whether the core skills for entry-level engineers have changed with GenAI and how the assessment of those skills has changed. Through two surveys, we consider both the educational and hiring perspectives, drawing on responses from 56 educators and 24 hiring professionals across diverse geographic regions to identify gaps and misaligned expectations. We find that educators frame their GenAI policies primarily around academic integrity, whereas GenAI use in industry is near-universal, with organizational policy governing how it should be used rather than whether to use it. Policies on GenAI use during interviews are still emerging, and respondents are split on whether they prefer candidates who demonstrate GenAI skills. Both populations are moving toward GenAI-resistant assessments built on observable real-time interactions and higher-order tasks, and both agree on which skills matter most, rating critical evaluation of AI-generated output, responsible and effective use of GenAI tools, and the ability to learn and adapt independently highly. However, they disagree on graduate readiness in these skills: hiring professionals report larger gaps, with the disagreement concentrated on foundational, non-GenAI-specific skills.