AI 中文总结
研究生成式人工智能对入门级软件工程师核心技能及评估的影响，通过对教育工作者和招聘专业人士的调查，发现双方在政策、面试态度及技能重要性上有共识，但在毕业生技能准备程度上有分歧，尤其在基础非特定技能方面。
AI 中文摘要
生成式人工智能解决广泛软件工程任务的能力正在重塑软件行业，引发了当今入门级软件工程师应具备何种能力的问题。本文通过两项调查，从教育和招聘两个角度，收集了来自不同地理区域的56名教育工作者和24名招聘专业人士的反馈，以确定差距和期望偏差。发现教育工作者主要围绕学术诚信制定生成式人工智能政策，而行业中其使用几乎普及，由组织政策规定使用方式而非是否使用。面试中使用生成式人工智能的政策仍在形成，受访者对是否青睐展示相关技能的候选人存在分歧。双方都倾向于基于可观察的实时交互和高阶任务的抗生成式人工智能评估，且对最重要的技能达成共识，但在毕业生这些技能的准备程度上存在分歧，招聘专业人士认为差距更大，分歧集中在基础的、非特定于生成式人工智能的技能上。
英文摘要
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.
