2025 ASEE Annual Conference & Exposition

With the growing incorporation of machine learning (ML) in chemical engineering, students must develop proficiency in a range of tools and techniques. ML is widely applied in areas such as process optimization, predictive modeling of chemical reactions, material property prediction, and fault detection in industrial processes. Gaining expertise in these methods equips students to tackle complex challenges and drive innovation within the field. In 2018, I formed a research group focused on tackling a range of machine learning problems, despite the absence of formal programming instruction in the curriculum. To streamline our workflow and improve efficiency, we selected MATLAB for its machine learning toolbox, which minimized the amount of manual coding required for implementing and testing algorithms.

This poster examines how ChatGPT, an AI language model, has functioned as an educational tool to assist chemical engineering students in bridging the gap between their core discipline and machine learning, with a focus on MATLAB. It highlights examples of ChatGPT’s capabilities, such as offering step-by-step guidance for implementing ML algorithms in MATLAB, helping with code debugging, simplifying complex ML concepts, and providing personalized learning support. The aim is to inspire and accelerate efforts by other faculty looking to integrate machine learning into their courses or research projects.