Stony Brook University is exploring the integration of Artificial Intelligence (AI) into its biomedical engineering education curriculum. In collaboration with UCSD, Cornell, the University of Utah, the University of Illinois at Urbana-Champaign, and the University of Florida, the university aims to equip students with AI skills crucial for their future careers. Findings from this collaboration have been published in the "Biomedical Engineering Education" journal under the title "Integrating Generative Artificial Intelligence Tools and Competencies in Biomedical Engineering Education."
David A. Rubenstein, Associate Professor in the Department of Biomedical Engineering and Associate Dean for Academic and Student Affairs in the Graduate School, has noted that, "Generative AI hit the world in a big way about a year or two ago." He is leading efforts to proactively incorporate AI into the curriculum, ensuring students are trained in the use of these tools. AI's rapid evolution presents challenges for curriculum development, requiring institutions to continuously adapt to new advancements.
Rubenstein has conducted conference working sessions to gather insights from approximately 100 universities on integrating AI into education. He explained the importance of aligning AI integration with professional and workforce development goals, noting that accreditation bodies like the Accreditation Board for Engineering and Technology are slow to adapt. "We need to think of this as a professional and workforce development tool for our students and implement it into the curriculum in a way that benefits the students’ careers," said Rubenstein.
The initiative emphasizes the need for universities to treat AI as an augmentation tool, not as a sole solution for success. According to Rose Tirotta-Esposito, Director for the Center for Excellence in Learning and Teaching, "Given AI’s varying impact across disciplines, it is essential that we collaborate to explore how to better empower every learner to thrive." Tirotta-Esposito highlighted the necessity of adapting teaching practices to connect with the evolving demands of the professional world.
AI's rapid advancements make it challenging to base educational recommendations on historical successes or failures, and there exists a shortage of established literature on best practices. Rubenstein stressed the need for an approach that continuously monitors AI's evolution to ensure curriculum relevance: "Even if we come up with great ideas of how to incorporate it, these methods can become dated pretty quickly."