Abstract

Enterprise Resource Planning (ERP) education has relied on static case studies that inadequately represent the complexity and uncertainty of the real-world enterprise environments. The learning materials provided by the SAP University Competence Center (UCC) effectively introduce foundational concepts of business processes; however, they limit students’ ability to transfer process knowledge into a cross-functional business context. Prior work has explored gamification to enhance motivation (Patwary et al., 2025; Wang et al., 2024), yet the potential of large language models (LLMs), specifically agentic AI in ERP learning/training, remains largely unexamined, despite their growing integration into modern enterprise platforms (Haki et al., 2025). To address this gap, this project will develop a GenAI-embedded dynamic learning platform that leverages LLMs to support learners’ interactions with enterprise software through adaptive, real-time, and contextually grounded training scenarios. Unlike predefined ERP simulations or static instructional cases, our artifact will enable the dynamic recombination of enterprise contexts, allowing learners to encounter diverse and evolving business situations that more closely resemble real-world business processes based on users’ profiles and learning goals. Rather than assuming AI integration inherently enhances learning outcomes, this research will explicitly examine whether learning improvements occur and how learning systems should be designed to enable such improvements. Grounded in Situated Learning Theory (Lave and Wenger, 1991), this platform is designed to scaffold learners’ reasoning and support contextualized problem solving. Using a Design Science Research methodology, the project will iteratively design, evaluate, and refine the learning artifact to derive design principles explaining how AI-augmented learning systems can effectively support conceptual learning in complex enterprise software environments. Beyond ERP education, the resulting design principles may inform the development of AI-augmented learning systems for other complex digital learning environments. A demonstration of the artifact will be provided at the AMCIS conference.

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