2025 ASEE Annual Conference & Exposition

The ISP and GenAI Crossroads: A Student Crossroads Between Information Searching and Ethics in Intro to Engineering Courses

Presented at Engineering Libraries Division (ELD) Technical Session 3

Background:

Generative artificial intelligence (GenAI) is increasingly becoming an integral part of instructional practices in higher education courses [1]. The prominence of GenAI has sparked a need to build understanding around the experience and application of GenAI within research, specifically how GenAI can affect students’ information search process. Over time, the field of library science has used the Information Search Process (ISP) model to examine the stages of the user experience in the search process. ISP examines information seeking through the lens of affective (feelings), cognitive (thoughts), and physical (actions) components [2]. Research has demonstrated that information seekers are repeatedly required to evaluate information to determine its relevance, appropriateness, accuracy, and completeness [3]. The information obtained from GenAI tools has unclear authority and source information, so users are burdened with double-checking or validating its output. The overlap of the foundational principles within the ISP, specifically the affective, cognitive, and physical, needs further research and analysis to determine the information-seeking experience while utilizing GenAI for academic purposes.

Purpose:

This study aims to use the ISP to map the student experience utilizing GenAI tools for research in course assignments. This research will begin to examine the relationship between GenAI and ISP, and pinpoint common student experiences, to guide librarians and professors toward better GenAI-assisted research instruction.

Methods:

This research is an ongoing collaboration between an Engineering Fundamentals professor and the Engineering Librarian. In this study, we took a purposive sample of students in an Introduction to Engineering course and conducted semi-structured interviews regarding the student experience of course-integrated GenAI research in their class. grounded-constructivist model was used to uncover themes from interview transcripts. The initial data analysis was performed by both researchers individually, and potential themes were collaboratively discussed and identified.

Results/Findings:

Early findings suggest that students have appropriate emotions of doubt and frustration about the accuracy of data produced by GenAI. Students’ also regularly described consideration of the quality of GenAI output (cognition), indicating that they possess some GenA information literacy. Student actions varied from (1) acceptance of poor-quality information to (2) prompt modification to (3) validation with external sources to (4) avoidance of GenAI altogether.

Conclusions:

A better understanding of the student experience utilizing GenAI for research will help to increase understanding and hopefully result in more structured instructional practices for integrating GenAI through librarian-professor collaborations and beyond. ISP analysis revealed that students gained some information literacy from course instruction leading to appropriate emotions, cognition, and actions towards the technology. The ISP model is useful to help understand student information literacy, as well as their information search process when engaging with GenAI during coursework.

Authors

Dr. Kristina Bloch University of Louisville [biography]

Dr. Kristina Bloch is the Engineering Librarian at the University of Louisville. She received her BA in Political Science in 2009 and her MA in Curriculum & Instruction with a focus on Library Science in 2011 from Tennessee Tech University. She received her second MA with an EdS in School Administration from Bellarmine University in 2014. In 2023, she received her EdD in Leadership from the College of Education at Spalding University. Before starting at UofL in February, she was a K-12 Librarian and STEM teacher for the past 13 years. Dr. Bloch has a passion for instruction, research, and student success and is excited to continue to learn and grow serving as the Engineering Librarian for UofL’s Speed School.
Dr. Campbell R Bego http://orcid.org/0000-0002-8125-3178 University of Louisville [biography]

Campbell Rightmyer Bego, PhD, PE, is an Assistant Professor in the Department of Engineering Fundamentals at the University of Louisville's Speed School of Engineering. She obtained a BS from Columbia University in Mechanical Engineering, a PE license in Mechanical Engineering from the state of New York, and an MS and PhD in Cognitive Science from the University of Louisville. With a broad goal of improving learning and persistence in undergraduate engineering programs, her current research focuses on (a) generative AI and information literacy for engineering, (b) individualized, first-year persistence interventions, and (c) the effectiveness of evidence-based practices in the engineering classroom.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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