Online Faculty Experiences with Implementing LLM and AI Tools in Online Academia: A Qualitative Exploratory Case Study
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Authors
Elliott, Jennifer
Issue Date
2026-03
Type
Dissertation
Language
en
Keywords
LLM , Faculty instructional practices , Online higher educatio , AI tools , Business, Engineering, Science, & Technological Innovation , Educational Leadership & Learning Lifelong
Alternative Title
Abstract
In fully online higher education, the use of large language model artificial intelligence (LLM-AI) tools created new instructional considerations related to teaching, learning, and academic integrity. The problem addressed in this study was that many faculty members teaching in online higher education viewed LLM-AI tools with skepticism, which often led to concerns regarding academic integrity, a perceived decline in instructional quality, and resistance to shifting pedagogical roles. The purpose of this qualitative exploratory case study was to investigate the experiences of online faculty members in successfully implementing LLM-AI tools in their classrooms to support student learning. Transformative Learning Theory served as the theoretical framework for the study. Data were collected through semi-structured interviews with eight online faculty participants representing varied disciplinary contexts, followed by a virtual focus group to support triangulation. Data were analyzed using Braun and Clarke’s updated seven-phase reflexive thematic analysis framework. The study was guided by two research questions: (1) What strategies did online faculty use to implement LLM-AI tools? and (2) How did online faculty implement LLM-AI tools in their classrooms to support student learning? Four themes were developed from the analysis: Purposeful and Intentional AI Integration, Explicit Boundary-Setting for AI Use, AI as a Support for Student Learning Processes, and AI as a Tool for Instructional Efficiency and Support. The results showed that online faculty implemented LLM-AI tools through intentional, ethically grounded instructional practices that emphasized pedagogical alignment, transparency, scaffolding, and efficiency-oriented support. Implications for practice included the need for clearer institutional guidance, expanded faculty development, and structured approaches to ethically responsible LLM-AI integration in fully online higher education.