ORCID
0000-0002-4199-0843
Keywords
Fraction comprehension, elementary education, AI-powered learning, personalized learning, student interest, mathematics education
Abstract
Fraction proficiency is crucial for foundational mathematics and broader STEM fields. This dissertation investigates the integration of Artificial Intelligence (AI) in mathematics classrooms, with a primary focus on students with mathematical learning challenges. AI technologies offer unique advantages for personalizing instruction and providing tailored feedback. The empirical study in the dissertation addresses a gap in the research related to AI's application during primary school fraction instruction.
The first manuscript presents a comprehensive literature review of AI in mathematics education, synthesizing research from 2020 - 2024. The second manuscript describes a quasi-experimental study evaluating Mathbot, an AI-based personalized learning platform. Repeated Measures ANOVA revealed modest improvements in fraction comprehension for students using Mathbot compared to traditional instruction, though changes in situational interest were not statistically significant. The third manuscript explores the application of AI-powered personalized learning in special education, providing strategies for pre-service teachers.
This dissertation illustrates AI's potential in mathematics education, while highlighting the need for further research to evaluate its effectiveness. It offers insights related to leveraging AI-driven learning to enhance outcomes for students, particularly those with learning challenges. Findings included herein will contribute to the evolving landscape of AI in special education.
Completion Date
2024
Semester
Fall
Committee Chair
Marino, Matthew
Degree
Doctor of Philosophy (Ph.D.)
College
College of Community Innovation and Education
Department
Learning Sciences and Educational Research
Degree Program
Exceptional Student Education
Format
Identifier
DP0028995
Language
English
Release Date
12-15-2024
Access Status
Dissertation
Campus Location
Orlando (Main) Campus
STARS Citation
Holman, Kenneth, "Exploring Fraction Comprehension and Interest in Elementary Education Through AI-Powered Personalized Learning" (2024). Graduate Thesis and Dissertation post-2024. 32.
https://stars.library.ucf.edu/etd2024/32
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