Date of Award

2026-05-22

Degree Name

PhD Leadership Studies

Dissertation Committee

Fred J. Galloway, EdD, Chair Robert Donmoyer, PhD, Member Nathan Swett, EdD, Member

Keywords

Generative AI, Large Language Models, ChatGPT, NotebookLM, Student Engagement, Intrinsic Motivation, Online Higher Education, Cognitive Theory of Multimedia Learning, Cognitive Load Theory, Zone of Proximal Development, Expertise Reversal Effect, Instructional Design, Mixed-Methods Research, AI Study Companions, Cognitive Scaffolding

Abstract

ABSTRACT

The rapid emergence of Generative AI (GenAI) large language models (LLMs) such as ChatGPT in higher education has left many institutions unprepared to assess their potential benefits and risks. This mixed-methods study examined how ChatGPT and Google’s NotebookLM were integrated into online course development and delivery at a small private nonprofit university in California, and assessed their perceived impact on student engagement, motivation, and learning.

Data were collected from post-course Likert-scale surveys completed by students across three online master’s-level courses and from pre-course prediction surveys by instructors in the two NotebookLM courses. Quantitative analyses included one-sample t-tests against a neutral midpoint, cross-course comparisons using several different inferential techniques, and Spearman rank-order correlations; open-ended responses were analyzed thematically.

Results indicated high overall levels of student engagement (M=4.31) and perceived learning (M=4.28). However, motivation varied significantly depending on the AI tool's pedagogical positioning. Courses that used NotebookLM as a "study companion" yielded significantly higher intrinsic motivation than courses that used ChatGPT as a transactional "productivity tool." Qualitative findings revealed that AI-generated audio features (podcasts) effectively managed cognitive load, supporting Mayer’s Cognitive Theory of Multimedia Learning. Furthermore, comparisons between faculty predictions and student outcomes highlighted an "Expertise Reversal Effect," in which instructional scaffolds intended for novices were occasionally perceived as unnecessary by proficient students.

The findings suggest that GenAI serves as an effective cognitive scaffold, enabling students to prevent cognitive overload while maximizing germane load processing and functioning as the 'more capable peer' within students' Zone of Proximal Development. The study concludes that institutions should frame AI as a collaborative partner to foster intrinsic motivation and enable personalized learning at scale rather than solely as an efficiency engine. Although the findings have limited generalizability, they may help address gaps in course design and development in this new frontier of AI integration in universities, facing the rapid adoption of LLMs. The research addresses key priorities in higher education by focusing on student engagement, motivation, and learning outcomes to generate insights that should help shape course-level AI integration policies and guidelines for the effective use of large language models in online higher education.

Document Type

Dissertation: Open Access

Department

Leadership Studies

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