Exploring the Impact of Artificial Intelligence Tools on Special Education Teachers’ Workload and Burnout in Colorado: A Qualitative Phenomenological Study
Year: 2026
Type: dissertation
Abstract: In the United States, special education (SPED) teachers face complex workloads, high caseloads, and extensive documentation responsibilities, contributing significantly to burnout and retention issues. The recent integration of artificial intelligence (AI) into educational settings presents promising opportunities to alleviate teachers' instructional and administrative demands. To better understand the impact of AI on SPED teachers, this phenomenological qualitative study explored the lived experiences of licensed K–12 SPED teachers in Colorado who use AI tools in their professional practice. This study also examined how AI tools supported workload management, as well as their perceived impacts on burnout and intentions to remain in the field. Data were collected using semi-structured interviews and voice memos. Findings provide detailed insight into how licensed K–12 SPED teachers utilize AI tools to manage workload demands, including instructional planning, communication, and required documentation, and how the use of these tools influences retention and SPED teacher shortages in the state of Colorado. Findings from this study can inform professional development and leadership recommendations for responsible AI use among special educators as well as reduction of workload demands. Although findings are limited to a sample of six SPED teachers from one geographic region, these findings have important implications for future research on AI use across diverse special education settings and teacher experience levels, workload reduction, and teacher retention.
Author: Rawa Abu Alsamah
Institution: University of Denver
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Funder: University of Denver
OpenAlex ID: W7214767402