Advancing Problem-Based Learning in Biomedical Engineering in the Era of Generative AI

TL;DR

The paper addresses how to integrate AI education into biomedical engineering curricula amid rapid AI advances and privacy concerns. It presents a three-year case study (2021–2023) at Georgia Tech and Emory implementing an advanced PBL framework for biomedical AI education across graduate and undergraduate cohorts, totaling 41 graduate teams and 21 undergraduate teams. The study reports 16 student-authored publications, consistently high peer evaluations, and state-of-the-art computational solutions, with industry partners providing GPUs and cloud resources. It offers a practical roadmap for institutions to integrate robust AI education within BME curricula and to train engineers to responsibly apply AI in healthcare.

Abstract

Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking and real-world knowledge application among students. With biomedical engineering rapidly converging with artificial intelligence (AI), integrating effective AI education into established curricula has become challenging yet increasingly necessary. Recent advancements, including AI's recognition by the 2024 Nobel Prize, have highlighted the importance of training students comprehensively in biomedical AI. However, effective biomedical AI education faces substantial obstacles, such as diverse student backgrounds, limited personalized mentoring, constrained computational resources, and difficulties in safely scaling hands-on practical experiments due to privacy and ethical concerns associated with biomedical data. To overcome these issues, we conducted a three-year (2021-2023) case study implementing an advanced PBL framework tailored specifically for biomedical AI education, involving 92 undergraduate and 156 graduate students from the joint Biomedical Engineering program of Georgia Institute of Technology and Emory University. Our approach emphasizes collaborative, interdisciplinary problem-solving through authentic biomedical AI challenges. The implementation led to measurable improvements in learning outcomes, evidenced by high research productivity (16 student-authored publications), consistently positive peer evaluations, and successful development of innovative computational methods addressing real biomedical challenges. Additionally, we examined the role of generative AI both as a teaching subject and an educational support tool within the PBL framework. Our study presents a practical and scalable roadmap for biomedical engineering departments aiming to integrate robust AI education into their curricula.

Figures (2)

• Figure 1: Overview of the five-stage Problem-Based Learning (PBL) model for biomedical AI education. The process starts with Problem Definition, during which students identify and clearly define real-world biomedical challenges. This is followed by Literature and Resource Exploration, where students collect relevant background theories and data. The third stage, Skill & Requirement Analysis, involves determining essential methodologies and tools for addressing the identified problems. In the Collaborative Inquiry and Solution Development stage, teams collaboratively develop, prototype, and refine their solutions. Finally, Reflection and Evaluation provides opportunities for continuous learning and critical assessment, reinforcing knowledge integration and iterative improvement.
• Figure 2: Conceptual framework of PBL integrated with BME and Generative AI. PBL serves as the central pedagogical model, supported by BME components including real-world clinical challenges, hands-on simulation activities, interdisciplinary collaboration, and technological innovations. Concurrently, generative AI methods—including knowledge synthesis, predictive modeling, personalized instruction, and AI-assisted collaboration—augment and modernize the PBL approach, facilitating an advanced, scalable, and tailored educational experience.