Machine learning (ML) and Artificial Intelligence (AI) components are increasingly embedded in software products, yet undergraduate software engineering (SE) curricula rarely provide systematic preparation for building, testing, deploying, and maintaining AI/ML-based software systems. This paper aims to provide evidence-based guidance for integrating AI/MLrelevant content into core SE education. We compile and define a structured inventory of topics relevant to SE practice in AI/MLbased software, then map these topics against required courses in a set of representative SE curricula to identify coverage gaps. To assess educational priorities and feasibility, we survey SE instructors on topic importance and integration constraints. Based on the crosswalk between topic definitions, curriculum coverage, and instructor prioritization, we derive a guideline that recommends where and how high-priority topics can be embedded within existing SE courses.

翻译：机器学习（ML）和人工智能（AI）组件越来越多地嵌入到软件产品中，然而，本科软件工程（SE）课程体系却很少系统性地培养学生构建、测试、部署和维护基于AI/ML的软件系统。本文旨在为将AI/ML相关内容整合到核心软件工程教育中提供循证指导。我们整理并定义了一份结构化的主题清单，涵盖基于AI/ML的软件中与SE实践相关的主题；随后，将这些主题与一系列代表性SE课程体系中的必修课程进行对照，以识别教学覆盖的缺口。为了评估教育的优先顺序和可行性，我们调查了SE教师关于主题重要性和整合限制的看法。基于主题定义、课程覆盖与教师优先级排序之间的交叉分析，我们得出了一份指导方针，推荐将高优先级主题在现有SE课程中的嵌入位置与嵌入方式。