Generative AI Expands the Intellectual Reach of Course Based Undergraduate Research Experiences (CUREs)

Explainable & Ethical AI
Published: arXiv: 2608.27638v1
Authors

Aditi Babar Kristin J. Davin Alex Dornburg

Abstract

Course-based undergraduate research experiences (CUREs) broaden access to authentic scientific inquiry through responsive instructor support as research problems become increasingly complex. Generative artificial intelligence (GenAI) may extend this support by providing individualized assistance that can adapt as student needs change. However, how embedding GenAI within a CURE to provide support across the research process impacts student inquiry, collaboration, and scientific reasoning remains unresolved. Here we use longitudinal qualitative data collected across three semesters of a bioinformatics and genomics CURE to show that GenAI expanded the intellectual reach of the research experience in three distinct ways. First, personalized, on-demand scaffolding allowed students to move beyond the boundaries of instructor expertise and transform their own interests into researchable inquiry, with all teams developing distinct self-directed projects rather than selecting instructor-provided topics. Second, GenAI became part of the distributed cognitive system of research teams, helping novice researchers communicate and coordinate across differentiated expertise without eliminating specialization. Third, expanded capability did not replace the need for disciplinary judgment. Students increasingly validated, revised, or rejected AI-generated contributions, such that research independence emerged through retained intellectual responsibility. Together, these findings suggest that GenAI can extend the reach of CUREs by expanding what novice researchers can investigate, how they can collaborate, and the level of responsibility they can assume while preserving human judgment central to authentic scientific inquiry.

Paper Summary

General Summary
This paper addresses important challenges in the field by introducing novel methodologies and approaches. The research contributes to advancing the state-of-the-art and has practical applications that could benefit various domains.
Paper Information
Categories:
cs.AI cs.HC
Published Date:

arXiv ID:

2608.27638v1

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