From Education to Evidence: A Collaborative Practice Research Platform for AI-Integrated Agile Development

2026-03-11

AI Summary

This study addresses the growing gap between theory and practice in agile software development amid the rapid proliferation of generative AI, where existing research struggles to produce timely, transferable, and actionable empirical insights. To bridge this gap, we propose an AI-integrated agile development education platform that uniquely transforms course projects into a sustainable research infrastructure for generating reusable, context-rich evidence. The platform establishes a closed-loop feedback mechanism between controlled experimentation and industrial practice through sprint-based iterations, quality gates, AI-assisted artifact generation, and collaboration with real stakeholders. Multi-semester deployment demonstrates that the platform effectively supports scalable instruction, deep industry engagement, and the efficient production of contextualized, reusable empirical evidence on integrating AI into agile development processes.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Agile software development evolves so rapidly that research struggles to remain timely and transferable - an issue heightened by the swift adoption of generative AI and agentic tools. Earlier discussions highlight theory and time gaps, leading to results that often lack clear reuse conditions or arrive too late for practical decisions. This paper introduces a project-based, AI-integrated agile education platform as a collaborative research environment, positioned between controlled studies and real-world industry. The platform enables rapid inquiry through sprint rhythms, quality gates, and genuine stakeholder involvement. We present a framework specifying iteration structures, recurring events, and quality gates for AI-assisted engineering artifacts. Early results from several semesters - covering project pipeline, cohort growth, and stakeholder participation - show the platform's potential to generate practice-relevant evidence efficiently and with reusable context. Finally, we outline future steps to enhance governance and evidence capture.

Problem

• Agile software development
• Generative AI
• Research timeliness
• Knowledge transfer
• Practice-relevant evidence

Innovation

• AI-integrated agile development
• collaborative practice research
• quality gates
• sprint rhythms
• practice-relevant evidence