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

Oenardi Lawanto

Committee

Yashin Brijmohan

Committee

Cassandra McCall

Committee

Joseph Furse

Abstract

The purpose of this study was to determine how the widespread use of Artificial Intelligence (AI) influences engineering students’ motivation and resilience. Specifically, the role of autonomy, competence, and relatedness as defined in Self-Determination Theory (SDT) was assessed to determine how these basic psychological needs interact with students’ AI use and motivation or resilience. The following research questions guided the study: (1) To what extent do the three basic psychological needs defined in SDT (autonomy, competence, and relatedness) mediate the relationship between the frequency of AI use and students’ levels of motivation (on the self-determination continuum) or resilience in an undergraduate engineering student population? (2) Which types of AI use are most strongly associated with higher or lower levels of self-determined motivation or resilience?

Using a cross-sectional quantitative research design, survey data was collected from students enrolled in statics, dynamics, and mechanics of materials courses. Data included information about students’ levels of intrinsic motivation, extrinsic motivation, and amotivation as well as their level of resilience and ways they use AI in their engineering courses. Analysis included mediation analysis and a series of independent samples t-tests. Results indicated that none of the three basic psychological needs defined by SDT significantly mediated the relationship between AI use and motivation or resilience. However, specific types of AI use that have a negative impact on students’ intrinsic motivation include summarizing textbook sections or lecture notes, explaining concepts, solving numerical problems, and using AI for moral support. These findings do not support complete regulation of the use of AI in engineering education, but they do imply a need to exercise caution with how extensively AI technology is used. Engineering educators should actively model appropriate uses of AI in ways that supplement students’ own self-directed learning such that it does not replace the students’ cognitive engagement with difficult engineering problem-solving.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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