Journal Article: Integrating Generative AI Into Librarian-Led Data Literacy Instruction For Science and Engineering Students

Data literacy and research data management (RDM) are essential competencies for science, technology, engineering, and mathematics (STEM) undergraduate students. Laboratory courses provide authentic contexts for undergraduate students to learn data curation skills. We evaluated a curriculum-integrated, librarian-led module in a senior biomedical engineering laboratory course that demonstrated strategies for using generative AI for RDM tasks and included practice exercises that guided students through using AI to assist with data cleaning and data processing. Students completed identical pre/post surveys, which included four open-response questions and three Likert-scale questions. Open responses were analyzed inductively using grounded theory, and Likert responses were analyzed using Welch’s t-test. Post-test responses reflected more detailed strategies for RDM and a shift toward using AI for cleaning and processing data rather than turning the data analysis process over to AI tools. We found no difference in students’ confidence in managing research data, but significant increases in students’ confidence in using AI tools for data management and using AI ethically within research. These findings suggest that curriculum-integrated, hands-on, librarian-led data literacy instruction can strengthen students’ knowledge of and confidence in using AI responsibly for data management and data curation.