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Dissertation
Working Smarter: A Quantitative Investigation into Higher Education Faculty's Perceptions, Adoption, and Use of Generative Artificial Intelligence (AI) in Alignment with the Learning Sciences and Universal Design for Learning
Doctor of Philosophy (Ph.D.), Drexel University
Jun 2024
DOI:
https://doi.org/10.17918/00010621
Abstract
Generative AI has been expanding at an exponential rate since its widespread emergence in late 2022, yet limited research has explored its adoption or use among higher education faculty. Guided by the Diffusion of Innovations theory, this quantitative survey research study investigated higher education faculty's perceptions, adoption, and use of generative AI, particularly in the context of their course design and delivery. The study also explored how generative AI has been leveraged by faculty to create more effective and engaging learning environments that align with Universal Design for Learning (UDL) principles and learning sciences research. Additionally, it assessed whether faculty perceptions, professional backgrounds, or demographic factors predicted their adoption or use of generative AI in alignment with UDL. Between mid-March and early April 2024, 214 higher education faculty members from diverse demographic and professional backgrounds participated in the study by completing an online questionnaire. Descriptive statistics revealed that 86% of participants had adopted generative AI in their work, and perceptions about the innovation differed between adopters and nonadopters. Participants reported using the technology for a variety of general and UDL-aligned purposes. Nested logistic regression analysis identified relative advantage and professional development on generative AI as significant predictors of adoption. Chi-square tests of independence revealed that men, tenured/tenure-track faculty, and those with moderate to significant knowledge of the learning sciences or generative AI were significantly more likely to adopt the technology. Furthermore, nested multiple linear regression models found professional development on generative AI and several perceived attributes of the innovation to be significant predictors of UDL-aligned use. Based on the findings and limitations, recommendations for future research were offered.
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Details
- Title
- Working Smarter
- Creators
- Ellana Black
- Contributors
- Kristen Betts (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- x, 158 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- School of Education (1997-2026); Drexel University
- Other Identifier
- 991021890314304721