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

Bibliographic Data

ID20295667
AuthorsMicky C Nnamdi (0009-0007-0915-6342, Georgia Institute of Technology), J Ben Tamo (0009-0003-3780-1047, Georgia Institute of Technology), Benoit Marteau (0000-0002-4177-7161, Georgia Institute of Technology), Wenqi Shi (0000-0001-8972-7342, Department of Health Data Science and Biostatistics, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA), May D Wang (0000-0003-3961-3608, Georgia Institute of Technology)
Year2026
Volume69
Issue2
Pages112-121
Publication date2026-04-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Education (JOURNAL)
Journal identifiersISSN: 0018-9359 • E-ISSN: 1557-9638
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/te.2026.3658007
OpenAlexW4417150028
LanguageEN
References cited72

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

Automatic summarization · Coding (social sciences) · Curriculum · Generative grammar · GRASP · Next Generation Science Standards · Personalized Learning · Scalability · Artificial Intelligence in Healthcare and Education · Biomedical and Engineering Education · Problem and Project Based Learning

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