BME7900 Seminar: Aditya Johri (George Mason)

Designing the Future of Engineering Education through Generative Artificial Intelligence Recent public narratives about artificial intelligence—ranging from widespread layoffs attributed to automation, to startups hiring AI agents as “employees,” to growing skepticism among students about the value of college degrees—underscore a moment of profound uncertainty for higher education. While technological hype is not new, engineering education now faces a unique convergence of forces that demand renewed attention. Against this backdrop, I explore critical questions for the future of engineering education: how curricula, assessment, and learning environments might evolve; when and how AI should be taught and used; and what distinctive value higher education can and should offer in an AI-mediated world. I argue that three interrelated factors make the current moment particularly consequential. First, the techno-cultural materiality of GenAI where it is deeply embedded in everyday sociomaterial practices. Second, the socio-cognitive affordances of GenAI which reflect a qualitative shift in computational capabilities mimicking highly human cognitive functions and raising foundational questions about knowledge, expertise, and learning. Third, the economic reality and pressures shaping higher education—student debt, employability concerns, and institutional financial constraints—that have intensified the perceived power of AI by aligning education ever more closely with workforce preparation. Drawing on secondary data, recent studies, and classroom-based research, the talk examines how students and faculty currently engage with generative AI. Overall, evidence shows widespread student use across cognitively complex tasks, persistent misconceptions about AI’s capabilities, and a tension between efficiency gains and reduced learning effort. Faculty adoption remains comparatively limited, shaped by unfamiliarity, ambivalence, and weak institutional guidance. Bio: Aditya Johri is a professor of information sciences and technology and Dr. Lawrence Cranberg Endowed Research Fellow in the College of Engineering & Computing at George Mason University. He studies how technology shapes learning across formal and informal settings and the ethical implications of using technology. He publishes broadly in the fields of engineering and computing education, and educational technology. His research has been recognized with several best paper awards and his edited volumes Cambridge Handbook of Engineering Education Research (CHEER) and International Handbook of Engineering Education Research received the Best Book Awards from Division I of AERA in 2015 and 2024, respectively. He served as a Fulbright-Nokia Distinguished Chair in ICT at Aalto University, Finland (2021) and he is a past recipient of the NSF Early Career Award (2009), the University Teaching Excellence Award (2002) and the Mentoring Excellence Award (2022) for undergraduate research at George Mason University. He was awarded a Ph.D. in learning sciences and technology design (2007) from Stanford University.