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Uncovering Information Behavior: AI-Assisted Citation Analysis of Mechanical Engineering Technology Senior Capstone Reports

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Conference

2024 ASEE Annual Conference & Exposition

Location

Portland, Oregon

Publication Date

June 23, 2024

Start Date

June 23, 2024

End Date

July 12, 2024

Conference Session

Engineering Libraries Division (ELD) Technical Session 4

Tagged Division

Engineering Libraries Division (ELD)

Permanent URL

https://peer.asee.org/48181

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Paper Authors

biography

Mark Chalmers University of Cincinnati

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Mark Chalmers is the Science & Engineering Librarian in the CEAS Library at the University of Cincinnati.

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biography

Aja Rachel Bettencourt-Mccarthy University of Cincinnati Orcid 16x16 orcid.org/0000-0003-4944-5097

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Aja Bettencourt-McCarthy is the Science and Engineering Global Services Librarian at the University of Cincinnati. Prior to joining the faculty at the University of Cincinnati, Aja was the STEM Instruction Librarian at the University of Kentucky Libraries and the Head of Public Services at the Oregon Institute of Technology Library. Aja earned an MLIS degree from the University of Washington and a Bachelor of Arts and Sciences in French and Community and Regional Development from UC Davis.

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Abstract

Citation analysis has been used by librarians and researchers to guide collection development decisions, assess information literacy, and to gain insight into the development of scholarship within a discipline. This project builds on this foundation by using citation analysis to better understand the information behavior of Mechanical Engineering Technology students.

For this project, librarians analyzed citations in Mechanical Engineering Technology (MET) capstone reports published in the last five years to better understand the sources students are using in their final undergraduate work. Given the scope of analyzing citations in more than 100 PDF documents, cutting-edge AI tools were piloted throughout the project to ease data collection and analysis and to explore the capabilities and limitations of these tools for similar research projects. The citation analysis conducted during this project provides insights into senior MET student information behavior and source use as well as a clearer understanding of whether these have changed over time. This information will help librarians to better support MET students and faculty by allowing for targeted information literacy instruction and outreach.

Chalmers, M., & Bettencourt-Mccarthy, A. R. (2024, June), Uncovering Information Behavior: AI-Assisted Citation Analysis of Mechanical Engineering Technology Senior Capstone Reports Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48181

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