Date of Award
8-2026
Document Type
Dissertation
Degree Name
Doctor of Education (EdD)
Department
Education Systems Improvement Science
Committee Chair/Advisor
Dr. Carlos Sandoval
Committee Member
Dr. Edwin Bonney
Committee Member
Dr. Brandi Hinnant-Crawford
Committee Member
Dr. Alla Polatty
Abstract
ABSTRACT
This study examines how one teacher in a large rural high school in South Carolina integrates an AI technological intervention as a supplemental tool, specifically as part of blended learning, to help increase math achievement for students in Geometry with Statistics. Evidence-based interventions, such as technology enhanced math instruction, encompass a diverse range of tools used by teachers, including an AI-driven high-dose tutorial which is a tool that educators use to diagnose, teach, and assess targeted skills. The key to the success of the AI tutorial is that it is programmed to use evidence-based practices (Fesler et al., 2026) like hyper-personalization for customized relevancy, an individualized math pathway, dynamically adjusted data-management pacing, higher-order questions, and real-time feedback. These practices are hard for teachers to maintain in the regular classroom, even when daily class routines and processes are well established and teacher functions are sharply in-tune with student needs (Er et al., 2024).
This study integrates the intervention during the first semester of the school year. Then, it attempts to determine if the intervention is maintained in the second semester, for a full grading cycle and for a new set of students. This study also attempts to determine if the intervention is effective in increasing math gain during a full semester. Student opinion about one specific best practice, feedback, is examined in three sources: teacher feedback, online live tutor feedback, and electronic feedback. This study looks at how easily the AI tutorial can be implemented in a classroom setting to increase math achievement, and examines the math teacher’s perceptions about integrating it. Edmentum AI HDT was found to be a high-impact practice in the context of this setting, with a McNemar’s test and p-value indicating a highly significant change. The teacher found it easy to use and worthwhile. A more robust design and statistics are needed to determine causal effects.
Recommended Citation
Warren, Barbara, "Technology Enhanced Math Instruction and Student Achievement" (2026). All Dissertations. 4325.
https://open.clemson.edu/all_dissertations/4325