Study Measures LLM Impact on Course Design and Student Learning
A new study from a New England university examines how generative AI and large language models are reshaping higher education. The mixed-methods research combines retrospective quantitative analysis, instructor surveys, and anonymous student surveys to document changes in faculty course development, student study methods, and grade reporting. Historical grade data from the university registrar is used to triangulate learning outcomes. The study aims to identify patterns in perceptions and experiences of LLMs as learning tools both inside and outside the classroom. Findings are based on multi-course data and seek to fill gaps in understanding the widespread but poorly documented impact of GenAI on academic practices.
Key facts
- Study uses mixed methods: quantitative analysis, instructor surveys, anonymous student surveys.
- Research conducted at a university in the New England region of the United States.
- Focuses on impact of LLMs on course design, student study methods, and grade reporting.
- Historical grade data from university registrar used for triangulation.
- Aims to document patterns in faculty and student perceptions of LLMs as learning tools.
- Multi-course study covering both inside and outside classroom use.
- Addresses lack of documentation on GenAI's impact on higher education.
- Published on arXiv under identifier 2605.16284.
Entities
Institutions
- arXiv
Locations
- New England
- United States