Award Date

5-15-2026

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Early Childhood, Multilingual, and Special Education

First Committee Member

Joseph Morgan

Second Committee Member

Heather Van Ness

Third Committee Member

Robert Smith

Fourth Committee Member

Kendall Hartley

Number of Pages

164

Abstract

Educators’ responsibilities extend well beyond instruction to include administrative duties such as grading, maintaining records, and documenting student behavior, as well as ongoing meetings and professional development requirements (Billingsley et al., 2019; Darling-Hammond et al., 2017). These demands can create a disconnect between role expectations and daily realities (Brunsting et al., 2023). Special education teachers (SETs) face these same responsibilities alongside additional paperwork requirements, which may account for up to 20% of their workday (Carlson et al., 2002; Suter & Giangreco, 2009; Vannest et al., 2011). A central task is the development of Individualized Education Programs (IEPs), including measurable annual academic and functional goals. To support goal development, schools have adopted tools such as goal-banking software aligned with SMART criteria (More & Hart Barnett, 2014; Müller et al., 2023). More recently, artificial intelligence (AI), including ChatGPT and other large language models, has emerged as a potential resource for both instructional and administrative tasks (Goldman et al., 2024; Kaplan-Rakowski et al., 2023). The Conservation of Resources theory (Hobfoll, 1989) provides a framework for understanding how such tools may reduce strain by offsetting job demands, particularly in high-stress fields like special education (Bettini et al., 2020; Cumming et al., 2021). This study examined the effectiveness of ChatGPT in supporting IEP goal writing and explored preservice teachers’ perceptions of its use. The literature review addressed three areas: goal-writing research and instruction, factors contributing to SET burnout (with emphasis on paperwork), and the role of AI in reducing teacher workload. A quasi-experimental pretest-posttest design was used to evaluate differences in goal quality and writing duration. Forty-four participants completed three phases: baseline, training, and intervention. Goal quality was assessed using the Revised Goals and Objectives Rating Instrument (R-GORI; Notari-Syverson & Schuster, 1995; Pretti-Frontczak & Bricker, 2000). Goal writing duration was assessed using a timer embedded in Qualtrics where participants submitted their goals. Results showed statistically significant improvements in goal quality and faster completion times. Participants generally viewed AI positively, citing efficiency, idea generation, and support during uncertainty, particularly for less experienced teachers. Some concerns were noted regarding overreliance and professional expectations. Limitations included inconsistent time tracking due to virtual pretesting, varied participant experience levels, and lack of data on revisions made during AI use. Future research should expand the sample, examine specific R-GORI indicators (e.g., gains in generality and measurability versus instructional context), and further investigate effective integration of AI in special education practice.

Controlled Subject

Special education; Special education teachers; Artificial intelligence--Educational applications

Disciplines

Accessibility | Education | Special Education and Teaching

File Format

PDF

File Size

2200 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Download


Share

COinS