Year of Award

2024

Document Type

Thesis

Degree Type

Master of Education (MEd)

Degree Name

Education

Department or School/College

Phyllis J. Washington College of Education

Committee Chair

Matthew Schertz

Commitee Members

Jason Neiffer, EdD Jonathon Richter, EdD

Keywords

artificial intelligence, motivation, automated feedback, Self-Determination Theory, feedback, online learning

Subject Categories

Online and Distance Education

Abstract

This thesis investigates the impact of an AI-powered educational tool on high school student motivation in asynchronous online courses using Self-Determination Theory (SDT) to examine perceived competence, autonomy, and relatedness. A pilot study involving 129 students utilized an AI-powered automated feedback (AF) writing review tool. Data was collected using the Autonomy and Competence in Technology Adoption (ACTA) Questionnaire and teacher communications. Findings indicate that AI tools can enhance perceived competence through immediate, personalized feedback. Still, the effectiveness is significantly influenced by the level of teacher communication, underscoring the need for a blended approach in online learning environments. Limitations include a small sample size and short duration, suggesting further research is needed. This study contributes to understanding how educators can balance AI and human feedback to enhance student motivation and learning outcomes in online learning environments.

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© Copyright 2024 Caitlin M. Byers