2026 ASEE Annual Conference & Exposition

This work-in-progress project explores the integration of human-centered artificial intelligence (AI) principles into undergraduate biomedical engineering education through a course focused on biomedical imaging. AI is rapidly transforming biomedical engineering, particularly in areas such as medical image analysis, diagnostics, and clinical decision support. As these technologies become more integrated into healthcare, undergraduate biomedical engineering programs must prepare students to understand both the technical and humanistic aspects of AI-driven systems. This project, supported by the National Science Foundation’s Research Initiation in Engineering Formation (RIEF) program, investigates how Human-Centered AI Algorithm Design (HCAD) can be used as an educational framework to promote ethical awareness, empathy, and professional formation in an undergraduate biomedical imaging course. HCAD applies principles of human-centered design to algorithm development by encouraging students to consider user needs, social context, and the ethical implications of AI. In this project, HCAD serves as both a technical and reflective framework for learning. Students engage in design-based learning activities where they develop and evaluate simple AI algorithms for medical image interpretation tasks while explicitly reflecting on the societal impacts of their design decisions. The overarching goal is to help students connect computational thinking and image processing skills with broader questions about fairness, accessibility, and trust in AI for healthcare applications. The study is being implemented at a predominantly undergraduate institution in a biomedical imaging course that introduces students to ultrasound and other medical imaging modalities. Students participate in HCAD-focused modules that integrate hands-on imaging data collection, basic machine learning workflows, and structured reflection on human and ethical considerations. A mixed-methods research design is being used to explore research questions related to how HCAD influences the professional formation of undergraduate engineering students. Data sources include pre- and post-course surveys, reflective essays, interviews, and analysis of design artifacts such as algorithm documentation and project reports. Together, these data will help characterize how students’ perceptions of AI, ethics, and professional responsibility evolve as they engage in human-centered design activities. This work contributes to the broader movement toward integrating responsible and equitable AI education into biomedical engineering curricula. By situating technical content such as image processing, feature extraction, and model evaluation within a human-centered framework, the project helps students see how technical and ethical considerations intersect in real-world healthcare contexts. As a work in progress, this project aims to generate early insights into how human-centered AI design can shape students’ professional identity and guide future curricular innovation in biomedical imaging education.