Semester of Graduation
Summer 2026
Degree Type
Dissertation
Degree Name
Instructional Technology
Department
School of Instructional Technology & Innovation
Committee Chair/First Advisor
Dr. Yeol Huh
Second Advisor
Dr. Laurie Dias
Third Advisor
Dr. Dabae Lee
Abstract
The Artificial intelligence (AI) technologies that generate automated feedback are increasingly present in educational environments. However, limited research has investigated teachers’ perceptions of the usefulness, reliability, and instructional value of AI-generated feedback. This qualitative study explored rural middle school teachers’ perceptions of AI-generated feedback and examined factors influencing their acceptance and potential classroom use of this technology. Guided by the Artificial Intelligence Technology Acceptance Model (AI-TAM), the study examined how perceived usefulness, perceived ease of use, and trust influence teachers’ attitudes toward AI-assisted feedback.
The study involved ten rural middle school teachers in northeastern Georgia in the United States. A qualitative instrumental multiple case study design facilitated an in-depth understanding of teachers’ perceptions and experiences. Data collection methods included semi-structured interviews and focus group discussions. Member checking procedures enhanced credibility, and data were analyzed using iterative coding and thematic analysis.
The findings indicated that teachers generally perceived AI-generated feedback as a potentially valuable instructional support, capable of improving efficiency and identifying patterns in student work. Nevertheless, participants raised concerns about the contextual accuracy, depth, and instructional alignment of AI-generated responses. Trust was identified as a central factor influencing teachers’ willingness to integrate AI-generated feedback into instructional practices. Participants stressed that AI should function as a supplemental tool supporting, rather than replacing, teacher expertise and professional judgment.
Revised Dissertation
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