Conference Session

Tech Session 3: Emerging Trends in Engineering Education: AI, Clean Energy, and Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Dayna Mandalyn Cline, United States Military Academy; David Zgonc, United States Military Academy at West Point; William B Vass, United States Military Academy; Michael A. Butkus P.E., United States Military Academy; Matthew Baideme, United States Military Academy; Brett Ryan Krueger, United States Military Academy

Tagged Divisions

Environmental Engineering & Sustainability Division (ENVIRON)

theprofessional engineer licensing process and is geared towards undergraduates completingaccredited programs [5]. The FE Environmental Exam consists of 15 sections coveringsupporting skills, including calculus, fluid mechanics, and thermodynamics, as well asenvironmental-specific topics such as biological wastewater processes, atmospheric modeling,and solid waste management.Generative artificial intelligence (genAI) may change current curriculum development processesin a way other technological advances have not. Publicly accessible, native language processinggenAI tools have expanded greatly since the release of OpenAI’s ChatGPT in November 2022.The U.S. Government Accountability Office estimated for the U.S. Congress in June 2024 thatmore than 100

Conference Session

Tech Session 3: Emerging Trends in Engineering Education: AI, Clean Energy, and Curriculum Design

Collection

2025 ASEE Annual Conference & Exposition

Authors

Matthew Yukio Takara, Carnegie Mellon University; Fethiye Ozis P.E., Carnegie Mellon University; Allison E. Connell Pensky, Carnegie Mellon University

Tagged Divisions

Environmental Engineering & Sustainability Division (ENVIRON)

purposes have been steadily developed in the decades since, with the majority of AIEdresearch focusing on instructor-side AI tools used for administrative tasks such as automatedgrading, feedback, and content creation [16–20]. There are some cases of AI tools developed forlearner use, but they are more application-specific and are very different from the modern, morerobust generative AI tools of the present study [21, 22]. Recent years have seen the emergence ofpowerful generative AI tools, such as OpenAI’s ChatGPT and Google’s Gemini (formerly Bard)released in late 2022 and early 2023, respectively. These tools are examples of powerful largelanguage models (LLMs) capable of interpreting human language inputs and generating outputsresembling