U-M Generative AI Use Cases

ACM (Association for Computing Machinery) students preparing for ICPC (International Collegiate Programming Contest) programming competitions and technical interviews often face challenges in finding relevant resources, receiving targeted feedback, and practicing in an interactive environment.

Websites need to meet accessibility standards and remove barriers for individuals using screen readers. Developers often spend significant time creating descriptive alt text for images, which is labor-intensive and prone to inconsistency.

Instead of searching through U-M’s standard practice guides (SPGs) on U-M’s HR website, ITS trained a Maizey to identify existing policies through plain text question prompting. The department’s goal was to simplify the process of searching for and accessing SPGs to save time and reduce the difficulty of finding relevant policies.

When it comes to transforming an engineering process from technical terms to logic, U-M GPT has become a helpful tool. U-M engineering and computer science students turned to the summarization ability of GenAI tools. This allows students to focus on the essence of design thinking as it relates to system building and problem-solving at a higher level.

Professor Anne Gere needed an efficient method to track down citations for her book, a task that can be time-consuming and tedious. Traditional methods of citation tracking were not sufficient to meet her needs for speed and accuracy.

The PMO needed to address several challenges, including the desire for a safer, more secure AI-driven solution for sensitive projects (e.g., HIPAA compliance) and the need for tools to aid in professional development. Team members, like Mallory Kwiatkowski, sought to enhance their skills and knowledge by leveraging outside resources and comparing various Generative AI (GenAI) tools, all while maintaining security and cost-effectiveness.

ITS Administration sought to efficiently rank and compare hundreds of resumes to streamline the hiring process and identify the most suitable candidates quickly.

Professor DeOrio sought to supplement existing course resources like office hours and discussion forums for his large EECS 485 Web Systems course, by providing quick feedback and assistance to students for one of their projects.

 Professor Jurgens wanted to change teaching practices to prepare students for the next generation in the era of AI. Specifically, Jurgens sought to help students understand AI not as a replacement for their work but as a tool for refinement, idea generation, critiques, feedback, and providing structure.

The U-M Service Center needed to reduce the time spent manually summarizing calls to their Tech Help line to improve efficiency and free up employee time for more critical tasks.

Professor Sabine Gabaron aimed to enhance student engagement and improve the quality of student presentations in her French 280 and 232 courses. The challenge was integrating AI tools to foster creativity and individuality while maintaining critical thinking skills. Additionally, data on student attitudes toward GenAI was needed to understand its impact and address any concerns.

The Engineering Advising Center aimed to enhance the availability and efficiency of academic services and support for first-year and undeclared engineering students. The challenge was to provide continuous, accessible academic advising that could handle routine inquiries and tasks, thereby allowing human advisors to focus on more complex and personalized student needs.

Faculty member Michael Hess aimed to address the issue of repetitive questions during office hours before exams in his SI564 course on SQL and Databases. The goal was to reduce the workload on both students and faculty by providing a more efficient way to handle frequently asked questions and allow students to access information at any time.
 

Assistant Professor Barbara Ericson aimed to improve the learning experience for students tackling close-ended write-code problems. Traditional methods often struggle to provide personalized, real-time feedback that aligns with students' individual solution paths. The challenge was to create a system leveraging U-M GPT Toolkit that could adaptively guide students through their coding errors and enhance their problem-solving skills

A graduate student from Mich Med Bioinformatics aimed to enhance the training process of deep reinforcement learning agents by generating reward signals through sentence evaluation. The challenge was to efficiently leverage a large-scale database of sentences and continuously obtain feedback from U-M GPT Toolkit, despite the requests-per-minute (RPM) limitation, which significantly impacted the training speed.

 Efficiently curating metadata with controlled terminology is a critical yet time-consuming task in social science data management. Data depositors often provide insufficient metadata, necessitating extensive enhancement by data repository staff. This process traditionally involves navigating a wide array of controlled terms, requiring substantial time, expertise, and sometimes the creation of new terms.

In large courses with multiple graders, maintaining consistent grading quality and limiting scoring variance can be challenging. Professor Peter Bodary aimed to address these issues by developing a system that assists human graders in ensuring more uniform grading standards and improving the clarity of grading criteria.

Transcribing museum specimen labels is a critical yet time-consuming task at the U-M Herbarium. The research group faced challenges in efficiently managing and processing the vast amount of data from specimen labels, which required meticulous transcription and significant manual effort.

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.

The Wolverine Learning Assistant addresses the challenge of making complex academic content more accessible and engaging for students at all levels of their educational journey across the University of Michigan's campuses. The goal is to transform how students interact with their coursework, improve their study habits, and effectively utilize campus resources.

The LSA Newnan Academic Advising Center sought to improve accessibility and efficiency in student advising services, particularly in light of challenges such as the COVID-19 pandemic and the non-mandatory nature of advising. They aimed to offer an innovative solution that could provide immediate, reliable information to students while maintaining the option for personalized advice through human interaction.

The U-M Maizey "Sandbox" addresses the need for round-the-clock assistance for both faculty and students. It aims to provide reliable and instant access to course-specific knowledge, thereby supporting students who miss classes, need clarification on lectures, or seek help outside of traditional office hours.