Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints. While machine learning courses emphasize model development, students often lack experience in architectural design, deployment, and monitoring of AI-enabled systems. Empirical evaluations of such system-oriented AI courses remain limited. This paper reflects on the design and implementation of a project-based master's-level course titled AI Algorithms: Theory and Engineering, at the University of Bremen, in which students developed a movie recommendation system while making architectural design decisions to address challenges related to scalability, deployment, and evolving requirements. We conducted a mixed-methods study combining analyses of student submissions and questionnaire responses to investigate integration challenges, learning outcomes, and opportunities for improvement. Our results indicate persistent difficulties in early architectural decisions, heterogeneous ML integration, evolving requirements, and data management, largely due to uneven ML and software engineering expertise. From the educator's perspective, the course fostered system-level reasoning and strengthened awareness of data-centric ML practices in AI-enabled systems.

翻译：教授面向AI赋能系统的软件工程需解决在真实约束下将AI组件融入完整软件架构的问题。尽管机器学习课程强调模型开发，但学生普遍缺乏AI赋能系统的架构设计、部署与监控经验。目前针对此类系统导向型AI课程的实证评估仍十分有限。本文反思了不莱梅大学研究生课程《AI算法：理论与实践》的设计与实施——学生在该项目中开发电影推荐系统，通过架构设计决策应对可扩展性、部署与需求演进等挑战。我们采用混合研究方法，结合学生作业与问卷反馈，探究集成难点、学习成效及改进方向。研究表明：早期架构决策、异构ML集成、需求演进及数据管理仍是持续性挑战，主要源于学生ML与软件工程能力的参差。从教育者视角看，该课程强化了系统级思维，并提升了学生对AI赋能系统中数据驱动型ML实践的认知。