Department of Engineering Fundamentals at the University of Louisville. He graduated from Furman University in 1992 with degrees in Computer Science and Philosophy. In 2008 he earned his Ph.D. in Computer Science Engineering from the University of Louisville. His research interest include cyber-security for industrial control systems and active learning. ©American Society for Engineering Education, 2025The Use of Generative AI for the Rapid Development of Qualitative Interview Transcripts for a Human-Centered Design ProblemAbstractThis paper describes how generative AI (i.e., ChatGPT) was used to rapidly develop fictitious,yet realistic, qualitative interview transcripts for industrial engineering
matching industry expectations.GAI, as defined by ChatGPT -a conversational AI model developed by OpenAI-, “refers to aclass of artificial intelligence models designed to create new content, such as text, image, audio,or video, that resembles human-generated data. These models "generate" content by learningpatterns from large datasets during their training process”. Given its capabilities and rapidadoption across industries, integrating GAI into technical training may become essential forpreparing students for the workforce.With this in mind, this research aims to identify Industrial Engineering (IE) areas with significantGAI activity and use these insights to explore how IE education can be enhanced to better equipgraduates for the evolving job
Biomechanics, Medical Devices, Clinical Imaging and Bioinstrumentation.Dr. Bhavana Kotla, The Ohio State University Visiting Assistant Professor, Department of Engineering Education, College of Engineering, The Ohio State University ©American Society for Engineering Education, 2025 Assessing the Impact of Generative AI in Developing and Using Grading Rubrics for Engineering CoursesAbstractEngineering education is rapidly integrating generative artificial-intelligence (GenAI) tools thatpromise faster, more consistent assessment—yet their reliability in discipline-specific contextsremains uncertain. This mixed-methods study compared ChatGPT-4, Claude 3.5, and PerplexityAI across
discussions in higher educationincluding its potential uses in and beyond the classroom. Initially, the focus was primarily onpreventing students from using generative AI tools, but attention is now shifting towardintegrating these tools into teaching and learning [1]. Many educators are exploring ways toincorporate generative AI into instruction [2].Students are often assumed to be tech-savvy [3]. With the widespread use of tools like ChatGPT,they may also be perceived as competent users of generative AI. However, effectively using AIfor learning requires more than just basic digital literacy, which can impact both the learningexperience and its benefit. Therefore, studying students’ interactions with AI is important, as thefindings will shape how