Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course

• URL: http://arxiv.org/abs/2606.16842v1
• Date: Mon, 15 Jun 2026 15:21:54 GMT
• Title: Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course
• Abstract summary: 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.<n>Students developed a movie recommendation system while making architectural design decisions to address challenges related to scalability, deployment, and evolving requirements.<n>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.
• Score: 6.295638112781735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• Learning to Code with Context: A Study-Based Approach [0.28675177318965045]
  The rapid emergence of generative AI tools is transforming the way software is developed.<n>Project-based courses offer an effective environment to explore and evaluate the integration of AI assistance into real-world development practices.<n>This paper presents our approach and a user study conducted within a university programming project in which students collaboratively developed computer games.
  arXiv  Detail & Related papers  (2025-12-04T20:40:36Z)
• AI Integration In ERP Evaluation Across Trends and Architectures [0.0]
  Review will investigate the latest trends, models of computing architecture, and analytical methods applied in assessing the performance of AI-integrated ERP services.<n>It identifies critical performance metrics and emphasizes the absence of any standard assessment frameworks or AI-aware systems.<n>We put forward a theoretical model that brings AI-enabled capabilities into alignment with metrics in performance assessment for ERPs.
  arXiv  Detail & Related papers  (2025-11-13T04:29:44Z)
• Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications [13.876049229274114]
  AI-native applications are a new paradigm in software engineering that fundamentally redefines how software is designed, developed, and evolved.<n>Despite their growing prominence, AI-native applications still lack a unified engineering definition and architectural blueprint.<n>This study seeks to establish a comprehensive understanding of AI-native applications by identifying their defining characteristics, key quality attributes, and typical technology stacks.
  arXiv  Detail & Related papers  (2025-09-16T15:01:23Z)
• On the Evaluation of Engineering Artificial General Intelligence [5.802869598386355]
  We discuss the challenges and propose a framework for evaluating engineering artificial general intelligence (eAGI) agents.<n>We consider eAGI as a specialization of artificial general intelligence (AGI)<n>eAGI agents should possess a unique blend of background knowledge (recall and retrieve) of facts and methods.
  arXiv  Detail & Related papers  (2025-05-15T18:52:47Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Engineering Artificial Intelligence: Framework, Challenges, and Future Direction [0.2678472239880052]
  This paper introduces the "ABCDE" as the key elements of Engineering AI.<n>It proposes a unified, systematic engineering AI ecosystem framework.<n>By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI.
  arXiv  Detail & Related papers  (2025-04-03T04:30:10Z)
• A quantitative framework for evaluating architectural patterns in ML systems [49.1574468325115]
  This study proposes a framework for quantitative assessment of architectural patterns in ML systems.<n>We focus on scalability and performance metrics for cost-effective CPU-based inference.
  arXiv  Detail & Related papers  (2025-01-20T15:30:09Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.