2023 ASEE Annual Conference & Exposition

In the field of data science, advancements in the field of machine learning have led to programs developing high-level reasoning, intricate data understanding, and groundbreaking predictive models. Machine Learning (ML) research aims at making a program ‘learn,’ that is, develop models and techniques with known information to be able to handle future problems. Traditionally, this is done by increasing the quantity and quality of input data and training the learner in more effective ways to interpret that information. This has a direct parallel to the collegiate classroom, as instructors aim to inspire mastery over a topic to their students through a variety of methods (homework problems, examinations, projects, etc.) and teach them the corresponding skillsets from feedback on these assignments. Machine Teaching (MT) research, on the other hand, aims at making the teacher more productive by using their own cognitive models to improve the quality of the data holistically. Again, this has a corresponding counterpart to current teaching pedagogies; the instructor decides on the details of an assignment from their own knowledge and experience with the end goal of having students retain the information and apply it to future problems. This paper identifies how the various innovations, lessons, and conclusions discovered in the field of artificial intelligence can enhance the quality of a collegiate classroom experience and improve student performance.