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Public Health Learning Technology Fellow
Summer 2025 Student Fellowship Program
Center for Academic Innovation
Deadline to apply: Ongoing, however, first consideration will be given to those who apply by Sunday, May 4, 2025. Location: 317 Maynard St., Ann Arbor, MI 48104 (75% in-person fellowship experience)
Pay Rate: Undergraduate $17/hr, Master’s $20/hr, Doctoral $23/hr
Job Duration: 8-12 hours/week for the summer semester, with the possibility of extending the fellowship
Supervisor: Melissa McCurry (mmccurry@umich.edu)
Who we’re looking for:
The student fellows will support the integration, testing, and optimization of the generative AI tool LearningClues into public health courses by providing feedback on the tool’s effectiveness and contributing to its ongoing refinement. Their work may include evaluating the tool’s performance in real-time, identifying areas for improvement, developing learning resources, and generating training data to help fine-tune the model. Special attention will be given to complex or discipline-specific terminology—such as distinctions between epidemiology and statistics—to improve the tool’s alignment with course content and learning objectives.
What You’ll Do:
As the Public Health Learning Technology Fellow, you’ll have the opportunity to: - Evaluate the functionality and effectiveness of a generative AI tool used in and across two foundational public health courses
- Support development and user testing processes by providing insights from a student perspective
- Review AI-generated responses to determine if they accurately reflect and incorporate course materials (e.g., lecture videos, PDFs, lecture slides, course readings)
- Provide feedback and recommendations for improving the tool’s performance and relevance
- Develop or curate learning resources that help clarify key concepts, support student understanding, and address common gaps in comprehension
- Help refine the AI model by identifying issues such as multiple-meaning words and ensuring accurate, discipline-specific terminology
- Assist in fine-tuning the model by identifying additional data inputs or course materials that could improve performance
- Assist in the analysis of usage data and learning analytics to assess tool effectiveness and inform improvement
- Collaborate with faculty and staff to inform future iterations of the tool.
- Contribute to the creation of training data that enhances the AI’s ability to generate meaningful, context-aware content
- Interest in improving digital learning tools and educational technology innovation
How to apply:
To apply, please complete the Student Fellowship Application and upload your resume and a response to the following writing prompt: - In 250 words or less, describe your interest in educational technology or AI. How do you see tools like generative AI playing a role in student learning, particularly in complex or technical subjects?
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