Board 45: Generative Artificial Intelligence (GAI)-Assisted Learning: Pushing the Boundaries of Engineering Education.

Abstract Generative artificial intelligence (GAI) has long been used across various fields; however, its usage in engineering education has been limited. Some areas where GAI tools have been implemented in education include intelligent tutoring, assessment, predicting, curriculum design, and personalized student learning. The recent proliferation of CHATGPT and other GAI tools presents limitless possibilities for transforming engineering pedagogy and assessment. At the same time, there are challenges associated with implementation. Consequently, there is a need to conduct an empirical study to evaluate these tools' strengths, limitations, and challenges to highlight potential opportunities for their application in engineering education broadly and pedagogy specifically. This study presents an overview of ongoing efforts to integrate GAI as a pedagogical tool at a Land Grant R1 University on the East Coast of the United States. Also, we are hoping to collect a within-case study of instructors who have successfully implemented artificial intelligence in their classrooms and course design. Data will be collected from the instructors through classroom observations and interviews on their classroom implementation. These will be thematically analyzed. Also, a deep exploration of students' learning experiences using the GAI will be conducted using focus group discussions and end-of-the-semester reflection. Other data sources that will be thematically analyzed include the syllabus, student ratings for teaching effectiveness, and instructors' reflections. Consistent with a case study design, the multiple sources of data serve as triangulation for this study. Also, we suggest that the data upon which these GAI tools are trained should be inclusive so it could serve diverse learners. In addition, this work discusses the ethical considerations of using GAI for instructors and students. The next steps include collecting and analyzing data from multiple sources from the faculty and students. It is expected that the outcome of this study will provide data-driven evidence on the impact of GAI on learning, recommended pedagogical practices, and future research direction. Finally, this study will underscore limitations with GAI and suggestions for improving the tool as it is positioned to transform engineering education.