Advancing Engineering Education and Research with AI Text Generation Tools

University of Michigan - Engineering Education and Research

Problem

The University of Michigan aimed to explore how AI text generation tools, like U-M GPT, can be used to understand and portray stress and mental health within the engineering community. The project sought to integrate AI into qualitative research methodologies, addressing critical concerns about the impact of AI on human interactions and its potential to devalue these interactions. The goal was to identify best practices for using AI to yield high-quality data and analyses.

Audience

Researchers and educators in the U-M Engineering community. 

Outcome/Impact

The University of Michigan leveraged the power of U-M AI tools to advance engineering education and research. The project uncovered how AI can be effectively integrated into qualitative research methodologies by comparing AI-generated content to qualitative data from student interviews. This innovative approach aligned with the NSF's EAGER program's goals, introducing new research methodologies and providing valuable insights into the engineering community.

The study addressed critical concerns about AI's impact on human interactions and identified best practices for using AI to produce high-quality data and analyses. The insights gained from this project enhanced the understanding of stress and mental health within the engineering community, contributing to efforts to address longstanding challenges related to underrepresentation and low retention in the field. This project exemplifies the transformative potential of U-M AI tools in research and education, positioning the University of Michigan at the forefront of the AI revolution.