Abstract
This study demonstrates the impact of Generative Artificial Intelligence (GAI) in replicating the cognitive apprenticeship learning model in classroom teaching of complex concepts related to the mechanics of failure of solid materials. We explore the role of combining JupyterLab and ChatGPT in learning and teaching complicated problems of applied mechanics that have been historically beyond the reach of conventional teaching approaches. The objectives of using the software integration are five-fold: (a) to address the difficulty in explaining the interconnection of multiple variables that affect the solution and physical interpretation of solid mechanics problems from a holistic standpoint, (b) to discuss the core concepts of solid mechanics using a set of generalized problems and their solutions with easily modifiable input parameters, (c) to help students attain the ability to critically think about the course concepts and apply them to analyze complex problems, (d) to test students’ ability to apply the concepts covered in class to problems that are outside the scope of the course topics, and (e) to promote awareness of outlier situations wherein traditionally taught tools are inapplicable. The ultimate goal is to amplify course learning outcomes and actively engage students in learning, helping them prepare for the growing AI-inclined job market in the engineering sector. Our results show that GAI can play a significant role in replacing cognitive apprenticeship in traditional classrooms.
Presenters
Zubaer HossainInstructional Associate Professor, Department of Mechanical Engineering, Texas A&M University College Station, Texas, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
ChatGPT, JupyterLab, Active Learning, Mechanics
