Preparing Students for AI-Driven Agile Development: A Project-Based AI Engineering Curriculum

2026-03-10

AI Summary

This study addresses the disconnect in traditional engineering education between agile development and AI competency, which impedes effective response to the transformative impact of generative AI on software engineering. The work proposes and validates an integrated curricular framework that deeply embeds generative AI and agent-based tools throughout the software development lifecycle—including requirements clarification, architectural design, coding, and testing—across seven biweekly sprints. Students engage in authentic project-based learning with AI-augmented development, while oral assessments ensure foundational learning outcomes and ethical reflection is woven into engineering practice. Preliminary evaluation indicates that this approach significantly enhances students’ engineering capabilities in AI-augmented environments, highlighting a new paradigm for agile pedagogy that must adapt to the rapid evolution of AI tooling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Generative AI and agentic tools are reshaping agile software development, yet many engineering curricula still teach agile methods and AI competencies separately and largely lecture-based. This paper presents a project-based AI Engineering curriculum designed to prepare students for AI-driven agile development by integrating agile practices and AI-enabled engineering throughout the program. We contribute (1) the curriculum concept and guiding principles, (2) a case study of interdisciplinary, AI-enabled agile student projects, and (3) early evidence from a mixed-methods evaluation. In our case study, second-semester bachelor students work in teams over seven two-week sprints on a realistic software product. AI tools are embedded into everyday agile engineering tasks - requirements clarification, backlog refinement, architectural reasoning, coding support, testing, and documentation - paired with reflection on human responsibility and quality. Initial results indicate that the integrated approach supports hands-on competence development in AI-assisted engineering. Key observations highlight the need for agile teaching adaptations due to rapid tool evolution, the critical role of oral verification to ensure foundational learning. We close with lessons learned and recommendations for educators designing agile project-based curricula in the age of AI.

Problem

• AI-driven agile development
• engineering curriculum
• project-based learning
• generative AI
• agile software development

Innovation

• AI-driven agile development
• project-based learning
• generative AI in education
• AI-enabled engineering
• agile curriculum design