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

Abstract

Contribution: This article presents a modularized, implementation-ready problem-based learning (PBL) framework tailored to biomedical artificial intelligence (AI), with safe use of generative AI (GenAI) for knowledge summarization and coding support. A replication package (syllabi, milestones, rubrics, team procedures, and AI-usage templates) accompanies the framework.Background: PBL has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing student learning through critical thinking and real-world problem-solving. However, traditional PBL approaches demand considerable faculty resources, domain-specific expertise, and continuous curricular updates to align with rapidly evolving biomedical technologies. Recent advancements, including the 2024 Nobel Prizes awarded for AI-enabled discoveries, highlight the importance of comprehensive student training in AI. As BME rapidly converges with AI, integrating effective AI education into established curricula is necessary but faces challenges. These include diverse student backgrounds, limited personalized mentoring, constrained computational resources, and difficulties in safely scaling hands-on experiments due to privacy and ethical concerns associated with biomedical data.Intended outcomes: This article aims to improve students' grasp of biomedical AI concepts, increase proactive learning and teamwork, and address equity and scalability concerns in AI education through authentic real-world problem-solving experiences.Application design: A three-year case study from 2021 to 2023 across the Georgia Institute of Technology and Emory University engaged 248 students in interdisciplinary teams to address real biomedical AI problems. The curriculum positioned GenAI as both a class topic and a learning tool, governed by strict disclosure, source-anchoring, verification, and version-logging policies.Findings: This implementation coincided with measurable improvements in learning outcomes, evidenced by high problem-solving productivity (16 student-authored peer-reviewed publications), positive peer evaluations, and the successful development of innovative computational methods for real biomedical problems. This strategy not only prepares students for future healthcare innovation but also systematically addresses educational disparities and resource limitations inherent to conventional learning approaches. The study presents a practical and scalable roadmap for BME departments aiming to integrate robust AI education into their curricula.