Learning Modules in BME Machine Learning Course

Motivation

Technology is developing as such a fast pace that ethics and regulation on technology are struggling to catch up. Machine learning is one of these technologies that is revolutionizing our lives and our domain: Biomedical Engineering. AI/ML-enabled medical devices have appeared in batches since 2015, and as responsible engineers, we want to make sure that these devices provide great benefits to patients independent of their demographics.

Therefore, we developed two learning modules: a hands-on/lecture module that develops students' abilities in identifying and addressing bias in machine learning, and another lecture module that demystifies generative AI technologies.

Coding Results for Machine Learning Surveys

Current Stage

This project is active. There is no funding on this project, but we're doing it to improve the BIM 155 course.

For students: Please find the current teaching schedule for BIM 155 and register for this course if you are interested.

Would Like to Work on This?

You are welcome to propose working on this project as an undergraduate or graduate student, but the assessment portions of pedagogical projects can be more difficult to work on as an undergraduate student. However, both learning modules still need significant work, and we are happy to have someone improve the course with us. If you'd like to work on curriculum design, we are looking for someone that:

  1. Have great English skills, especially in writing and speaking (including making scientific presentations).
  2. Good at programming in Python and proficient in searching for the right data sets for the right task.
  3. Experience in teaching and tutoring will be a plus.

An undergraduate student may work on the assessment portions if they have no intention of taking BIM 155 and are not taking senior design. However, results from this project tends to be easier to work on, so the results of this projects are often used as practice material before you move onto more complicated tasks.

Peer-Reviewed Publications

  1. X. Wang, T. M. Chan, and A. A. Tamura. "A Learning Module for Generative AI Literacy in a Biomedical Engineering Classroom," Frontiers in Education, vol. 10, 2025, doi: 10.3389/feduc.2025.1551385
  2. X. Wang, T. M. Chan, and A. A. Tamura, "Work in progress: Preparing Biomedical Engineers to Tackle Biases in Machine Learning," 2025 ASEE Annual Conference & Exposition, 2025.

Peer-Reviewed Presentations

  1. X. Wang, "Infusing A Biomedical Engineering Machine Learning Course with DEI Content, " presented at the 2023 Scholarship of Learning and Teaching (SoTL) Conference.
  2. X. Wang, "A Pilot Project-Based Course in Machine Learning for Biomedical-Related Disciplines," presented at the 2022 Scholarship of Learning and Teaching (SoTL) Conference.