Learning to Use Generative AI and Using It to Improve Learning: A Systems Engineering Research Seminar Case Study

Abstract

The rapid advancement of generative artificial intelligence (GenAI) has significantly impacted educational and professional practices, presenting both opportunities and challenges. This study explores the integration of GenAI into a systems engineering seminar, aiming to develop essential GenAI skills and enhance disciplinary knowledge. Two hypotheses guide this research: (H1) engaging with GenAI in research and design activities improves student proficiency in using GenAI, and (H2) engaging with GenAI in design activities related to advanced disciplinary knowledge improves their understanding and use. The study employs a case study approach combined with a survey, involving 26 graduate students in a systems engineering seminar. Students were encouraged to use GenAI tools for all tasks, including literature reviews, presentations, and a drone design challenge. Data was collected through recorded presentations and student interactions with GenAI tools. Data analysis involved systematic coding and thematic analysis of presentations, student–GenAI interactions, and survey responses, with triangulation across multiple data sources to ensure validity. The findings indicate that the students effectively learned about GenAI tools, demonstrated gradual improvements in using tools, criticized and selected among them, and even built a new GenAI tool. They demonstrated improved critical thinking and creativity, as evidenced by their ability to critically assess GenAI outputs and apply them to practical challenges like the drone design task. One student developed a custom GenAI tool by training ChatGPT-4o for specialized modeling tasks. The integration of GenAI in educational settings through self-directed learning, peer presentations, and design challenges appears to enhance learning experiences by fostering critical thinking and creativity. The evidence suggests that GenAI tools, when used with appropriate validation and critical assessment, may serve as valuable aids in developing engineering skills and addressing complex problems. Best practices in teaching about GenAI are provided.