Introducing Data-Driven Materials Informatics into Undergraduate Courses through a Polymer Science Workshop

With the rapid growth of artificial intelligence and machine learning across scientific disciplines from materials discovery to data-driven problem solving, there is increasing opportunity to integrate these tools into a broad range of applications. Successful adoption of these approaches in research can be enhanced by foundational exposure during undergraduate education. The objective of this study is to introduce fundamental machine learning concepts to undergraduate students through a hands-on, application-focused workshop during a polymer science and engineering course. Students were guided through key steps of the machine learning workflow, including data cleaning, model training, performance evaluation, and result interpretation, using a polymer solubility data set generated via visual inspection. The effectiveness of the workshop was assessed through pre- and postworkshop student surveys, which indicated a measurable improvement in students' understanding and confidence in applying machine learning techniques. The integration of this workshop into a materials course introduces the students to the new concepts while extending the application of the course material.