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Student Presenter #1: Sage Maul

Title:
Translating between teaching undergrads and teaching in industry

Abstract:
Teaching in college settings is very different from teaching in industry, but there is little written about how instructors can pivot between the two settings. In this presentation, I will pull from my experience as an instructor in industry and from my semester of shadowing in a First Year Engineering classroom to discuss the distinct aspects of both. I will explore the unique challenges in both settings. For example, material in industry is generally incredibly applied compared to that in a college classroom. It is important for educators to cater their instruction to the type of audience they are teaching so that they can most effectively help their students.

Bio:
Sage Maul is a PhD candidate in Purdue’s School of Engineering Education. Sage’s research explores structural factors on student experiences for disabled students and in electrical and computer engineering courses. Sage graduated with a bachelor of science in Electrical Engineering from Purdue and worked in industry for five years before starting graduate school. Experiences with undergrad accommodations and an ADHD diagnosis as an adult inform Sage’s research work.

Student Presenter #2: Aadithan Anbuvanan

Title:
Exploring the Role of Generative AI in Reverse Engineering for Engineering Students

Abstract:
Reverse engineering and design optimization have been widely applied in engineering education. Nevertheless, these have limited applicability when linked to generative artificial intelligence. As part of this research, we have conducted a study that assesses the benefits of artificial intelligence approaches to reverse engineering. As part of our work, we developed a framework that uses generative artificial intelligence, employs reverse engineering, and incorporates a large language model. The learning experience was based on collaborative work using Aladdin, which assists users in building design and simulation, aiming to assess a building’s energy efficiency and to produce specific optimization variables.

Bio:
Aadithan Anbuvanan is a first-year PhD student in Purdue’s School of Engineering Education. He holds a bachelor’s degree in Automobile Engineering and a master’s degree in Industrial Engineering from Clemson University. His research interests include AI-enhanced engineering education and design thinking. His past work includes NLP model development for vehicle requirement analysis and optimization work for scheduling in flexible flowline systems.