2024 ASEE Annual Conference & Exposition

As science, technology, engineering, and mathematics (STEM) continue to advance, engineering education for both undergraduate and graduate students need to be updated accordingly. Undergraduate students benefit from exposure to the state-of-the-art technologies and techniques to successfully join an ever-changing workforce. Graduate students also benefit from learning about cutting edge tools to succeed in their research goals and contribute to their respective research communities. The artificial intelligence, modeling, and simulation (AIMS) certificate program was created at a mechanical engineering program of a large midwestern university to help address these missing components. The goals of the program are for students to learn reliable computer simulation and design under uncertainty, gain an understanding of the new pathways to achieve robust and affordable modeling with artificial intelligence and machine learning, and become proficient in utilizing hybrid models toward intelligent complex machines. Most AIMS courses include projects throughout the semester that provide a hands-on component to implementing course material. These projects help students develop a deeper understanding of the material, contribute to improved retention of knowledge gained, and encourage collaboration amongst students. This work aims to explore how participation in AIMS courses affects the development of students' engineering self-efficacy (ESE) and engineering judgement (EJ), in addition to what other factors may be related to ESE and EJ development.

ESE is an individual’s belief regarding their ability to achieve a specific goal based on their engineering knowledge, in this case specifically related to the content covered by AIMS courses. Past literature shows how one’s beliefs about their ability can influence their behavior and goal attainment. EJ is an individual’s capacity to determine and execute tasks that will lead to a predicted outcome. Judgement about capabilities is typically developed in parallel to engineering problem solving. In this study, ESE and EJ are measured via an online survey distributed during AIMS courses. To date, responses have been collected from 63 undergraduate and 15 graduate students. The survey also asks about student participation in various workshops held on campus that teach diverse topics that cannot be covered in class due to time constraints. Topics range from interacting with research participants and their data to hands on skills (soldering, building robots, etc.) and programming in various coding languages.

The study will also account for other independent factors that may influence the relationship between participation in AIMS courses and student’s ESE and EJ including the student’s academic standing (undergraduate or graduate), student participation in extracurricular activities (yes or no), and student interest in the AIMS certificate in general (yes, maybe, or no). Data will be analyzed using multiple regression statistical analysis to extract themes relating to the students’ ESE and EJ. A project from an AIMS course will also be described in detail to demonstrate how project-based learning is implemented in course work. Future work includes the continued collection of survey data.