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

Format

PDF

Identifier

DP0028995

Language

English

Release Date

12-15-2024

Document Type

Dissertation

Campus Location

Orlando (Main) Campus

Subjects

Artificial intelligence--Educational applications; Mathematics--Study and teaching (Elementary)--Research; Fractions--Computer-assisted instruction; Mathematics--Study and teaching--Computer-assisted instruction; Education, Elementary--Research

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