Date

12-16-2025

Department

School of Education

Degree

Doctor of Philosophy in Education (PhD)

Chair

Hoiwah Benny Fong

Keywords

AI-Assisted Learning, Large Language Model, Technology Acceptance Model, Perceived Usefulness, free vs paid

Disciplines

Education

Abstract

The purpose of this quantitative causal-comparative study was to determine if there is a difference in student perceived usefulness of large language models (LLMs) for academic achievement among undergraduate business students at different business technology course levels (200, 300, and 400) when comparing access to free versus paid LLMs. This study is important because it contributes to a practical understanding of the impact of LLM-assisted learning on students' perception of how LLMs contribute to academic achievement at the undergraduate level; therefore, this study contributes to the literature by providing empirical-based information on how a student perceives the effectiveness of using LLMs for educational outcomes. Insights into the extent to which students perceive LLMs, free versus paid, for improving academic performance may be beneficial when integrating these technologies within the educational environment and adapting them for improved learning. The sample was composed of 161 undergraduate business school students enrolled in 200, 300, and 400-level business technology courses at a medium-sized university in a southeastern state. Data was collected using the Technology Acceptance Model perceived usefulness survey sent via email using the Qualtrics cloud-based software platform. The results from the two-way ANOVA indicated no significant differences in perceived usefulness across course levels and LLM access types, nor interaction between course level and LLM access. These results indicate that for undergraduate business school students, free LLM access may be sufficient to support perceived academic usefulness. It is recommended that further research examine additional undergraduate student major areas of study to investigate possible trends.

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