通过电影推荐系统项目，培养学生构建真实AI系统的能力。
Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course
- 以项目驱动方式训练学生在真实约束下进行AI系统架构设计。
- 学生在可扩展性、部署和需求变化方面面临持续挑战。
- 适合希望提升系统工程能力的AI方向研究生或工程师。
教授面向AI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。尽管机器学习课程侧重模型开发，学生却常缺乏系统架构设计、部署与监控的经验。此类面向系统的AI课程实证研究仍较有限。本文反思了不来梅大学开设的一门硕士级项目课程《AI算法：理论与工程》的设计与实施，学生在开发电影推荐系统过程中需做出架构决策，应对可扩展性、部署及需求演变等挑战。我们通过混合方法研究，结合对学生作业的分析与问卷反馈，考察了集成难点、学习成效与改进空间。结果显示，早期架构决策、异构机器学习集成、需求演化与数据管理仍存在持续困难，主要源于机器学习与软件工程能力不均衡。从教育者视角看，该课程促进了系统级思维，并增强了学生对数据驱动型AI实践的认知。
原文摘要 · Abstract (English)
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.
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