2026 ASEE Annual Conference & Exposition

Against the macro backdrop of artificial intelligence profoundly reshaping global industrial landscapes and knowledge systems, engineering education in higher education institutions is poised for a historic paradigm shift. Driving systematic adaptive transformation has become the key pathway to enhancing educational quality and cultivating future-oriented engineering talent. This study, grounded in innovation diffusion theory and employing a multi-case research approach, first selects six universities of distinct types—research-oriented, teaching-research-oriented, and applied technology-oriented—to ensure representativeness and diversity. Through semi-structured interviews with administrators, faculty, and students, alongside multi-source data collection including syllabi and policy documents, it systematically explores integration pathways and adaptive mechanisms for incorporating AI into university engineering education. The study focuses on examining the impacts of AI technology introduction on engineering education's content, methodological frameworks, organizational structures, and evaluation systems. It attempts to identify key elements and conditions for transitioning from “early adopters” to “early majority” adoption. Building on this, the research identifies and summarizes adaptive models for integrating AI into university engineering education, further exploring the adaptive gap between technological iteration speed and educational system responsiveness, along with pathways to bridge this gap. Integrating AI into university engineering education requires systematic strategies to consciously enhance compatibility, reduce complexity, and highlight relative advantages. Throughout this process, universities must clarify the objectives of AI integration, provide technical support and faculty training, develop aligned curricula and teaching resources, and establish mechanisms for continuous feedback and optimization.