Award Date
5-15-2026
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Educational Psychology, Leadership, and Higher Education
First Committee Member
E. Michael Nussbaum
Second Committee Member
Omolola Odejimi
Third Committee Member
Vanessa Vongkulluksn
Fourth Committee Member
Vincent Hsu
Number of Pages
147
Abstract
This study examined whether structured ChatGPT tutoring could approximate the learning gains historically associated with one-on-one human tutoring. Grounded in cognitive load theory and informed by Bloom’s two-sigma benchmark, the investigation used double-entry accounting as a procedurally dense testbed requiring coordinated schema construction and rule-based reasoning. A randomized proof-of-concept design assigned thirty first-year accounting students to either human tutoring or ChatGPT tutoring across an eight-week instructional interval. Learning outcomes were assessed at pretest, posttest, and delayed retention. Repeated-measures analyses revealed substantial within-subject learning gains across both instructional modalities. Performance improved significantly from baseline to post-instruction assessment, indicating acquisition of procedural schemas. A significant Group × Time interaction demonstrated differentiated learning trajectories, with students receiving ChatGPT tutoring exhibiting steeper acquisition gains and greater performance stability across the retention interval. Nonparametric robustness analyses using the ANOVA-Type Statistic reproduced the same inferential pattern. Survey findings indicated high engagement and instructional clarity across both conditions. Within the constraints of a proof-of-concept design, the magnitude of gains observed in the ChatGPT condition approached levels historically associated with individualized tutoring, suggesting that principled instructional structure may narrow the gap between personalized and scalable academic support.
Keywords
AI Tutoring Systems; Bloom’s Two-Sigma Problem; Cognitive Load Theory; Learning Transfer and Retention; Procedural Learning
Disciplines
Education | Educational Assessment, Evaluation, and Research | Educational Psychology
File Format
File Size
1111 KB
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Affognon, Dieudonne A., "Solving the 2-Sigma Problem With ChatGPT: A Case Study on the Double-Entry Accounting System" (2026). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5495.
https://oasis.library.unlv.edu/thesesdissertations/5495
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Included in
Educational Assessment, Evaluation, and Research Commons, Educational Psychology Commons