Adaptive Troubleshooting in Engineering: Developing Cognitive Flexibility to Address Complex Challenges Among Undergraduates

This project aims to serve the national interest by improving how undergraduate engineering students learn to troubleshoot complex, real-world engineering systems. Modern technological systems, from power plants to spacecraft, are increasingly interconnected and rapidly changing, and the problems engineers encounter rarely match the well-defined exercises in undergraduate engineering coursework. Preparing engineers who can reason adaptively when standard procedures fall short is therefore a matter of national importance for public safety, economic competitiveness, and technological leadership. This Level 1 Engaged Student Learning project intends to strengthen students' capacity for adaptive troubleshooting by developing and studying a new educational platform, the Adaptive Troubleshooting Learning Environment (ATLAS). Building on decades of learning sciences research on how experts develop flexible knowledge, students using ATLAS will explore rich case studies of real engineering failures, examine each case from multiple perspectives, establish conceptual connections across cases, and practice solving novel problems with support from an AI-based conversational assistant to scaffold reasoning. By treating uncertainty as a normal and expected part of engineering practice, the project seeks to build confidence and persistence among students who might otherwise think that engineering is not for them. The significance of the project lies in its potential to prepare a more adaptable technical workforce and to share all case studies, software, and instructional materials freely with educators nationwide. The goals of this project are to advance understanding of the cognitive demands of complex engineering troubleshooting and to design, implement, and study educational interventions that foster adaptive troubleshooting capacity. The scope encompasses case development, learning environment design, and classroom research in undergraduate fluid mechanics courses. Grounded in Cognitive Flexibility Theory, the project team plans to create conceptually rich case studies of engineering system failures, and to develop ATLAS, an open-source, web-based hypertext environment integrated with a learning management system. Using ATLAS, students will navigate cases through shared tags and construct solutions to novel troubleshooting problems with support from a large language model-powered chatbot that reveals information in response to learner-generated questions, guided by a well-researched troubleshooting framework attentive to internal dynamics of technical systems and external constraints of workplace contexts. With formative and summative evaluation from an external advisory board, the research plan combines expert interviews and think-aloud pilot studies with randomized controlled trials investigating conditions under which improved student learning occurs. The goal is to examine the effects of abductive reasoning and inquiry-driven information retrieval on complex problem solving. Dissemination plans include faculty development workshops at American Society of Engineering Education conferences, public release of all software, and publications in engineering education venues. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.