Date of Award:

8-2026

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Engineering Education

Committee Chair(s)

Wade Goodridge

Committee

Wade Goodridge

Committee

Ning Fang

Committee

Oenardi Lawanto

Committee

Cassandra McCall

Committee

Joseph Furse

Abstract

Artificial intelligence tools like ChatGPT have quickly become part of everyday life for many people, including engineering students. This has raised concerns about the effects of students relying or over-relying on AI to help with their coursework. Are they still building the problem-solving and reasoning skills they will need as engineers? The purpose of this study is to understand not just whether engineering students use AI, but how they use it.

This research focused on two different ways students can use AI tools. 'Understanding-seeking' is when a student uses AI as a learning support such as asking thoughtful questions to explain a concept or check their thinking while they stay actively involved in the problem-solving process. 'Solution-seeking' is when a student uses AI as a shortcut such as asking it for the answer directly without engaging with the reasoning behind it. In this case, the student is allowing the AI tool to take over most of the cognitive effort.

This study surveyed 178 undergraduate engineering students taking a statics course (a foundational engineering class) at Utah State University over two semesters. Students answered questions about their background and their experience with AI. The AI Use and Perceptions survey asked students about their attitudes and perceptions towards AI, as well as how they use it. These questions are grounded in existing technology reliance and acceptance literature and research.

The findings of this study revealed that engineering students reported understanding-seeking use more often than solution-seeking use, but that a blend of the two use types still exist. Attitudes (familiarity and acceptance) were found to be linked to understanding-seeking use, while perceptions (cognitive congruence and perceived domain knowledge) were linked to solution-seeking use. Theoretically, this finding found that Acceptance (TAM) drives level of engagement with AI tools, but reliance constructs (TTD) describe how AI tools are used. Lower perceived domain knowledge was associated with solution-seeking use, suggesting that self-efficacy may play a role in how engineering students use and rely on AI tools. Frequency of use and demographic variables did not yield any significant differences in AI use.

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