TDNet Discover

The purpose of this study was to examine how AI-powered educational technologies influence teacher workload through secondary qualitative data analysis of professional articles, TED Talks, blogs, and district policy documents. The study investigated the ways AI-driven tools affect administrative responsibilities, instructional demands, and overall workload, highlighting both perceived benefits and limitations. Centering teachers’ lived experiences as represented in publicly available sources; this research contributed to ongoing discussions surrounding educational sustainability and teacher retention. A phenomenological research design was employed to capture the essence of shared teacher experiences and their interpretations of integrating AI-driven tools into professional practice. Data was analyzed using thematic analysis supported by ATLAS.ti, with coding progressing from open to axial phases to ensure analytic rigor while preserving the authenticity of language and context. The study was guided by the technology acceptance model (TAM) and complemented by Maslow’s hierarchy of needs (MHN), providing a theoretical lens for interpreting teachers’ responses to AI integration. Findings revealed four themes: (1) Teacher Workload and Efficiency; (2) AI for Instructional Workload Relief; (3) AI for Administrative Tasks; and (4) Implementation, Readiness, and Ethical Trust, indicating AI tools could meaningfully reduce administrative burden while simultaneously requiring teachers to renegotiate professional roles, competencies, and expectations, with direct implications for workload management and long-term teacher retention.