Theses and Dissertations

Date of Award

2026

Document Type

Dissertation

Degree Name

Doctor of Education (EdD)

Department

Abraham S. Fischler College of Education and School of Criminal Justice

Advisor

Roslyn Doctorow

Committee Member

Linda Gaughan

Committee Member

Kimberly Durham

Keywords

Amira Learning, AI-supported instruction, artificial intelligence, classroom conditions, classroom observations, classroom organization, collaboration, decision-making, elementary literacy education, evidence-based literacy instruction, foundational literacy skills, instructional adaptation, instructional decision-making, instructional time, kindergarten, learning environments, literacy instruction, literacy routines, professional development, professional expertise, professional learning, qualitative research, reading programs, semi structured interviews, student engagement, teacher experiences, technology reliability, thematic analysis

Abstract

This applied dissertation was completed to explore kindergarten through grade 2 teachers’ experiences implementing an artificial intelligence (AI)-supported reading program during literacy instruction. Guided by a generic qualitative design, the study was designed to examine how teachers prepared to use the program, integrated it into daily literacy instruction, used AI-generated data to inform instructional decision-making, and experienced the instructional and contextual conditions that supported or hindered implementation. Purposeful sampling was used to recruit 10 kindergarten through grade 2 teachers with experience using Amira Learning. Data were collected through semi structured interviews and analyzed using thematic analysis.

Four themes emerged from the findings. First, teachers described preparation as an ongoing process that extended beyond initial professional development through classroom experience, collaboration, and independent learning. Second, participants viewed implementation as a process of instructional adaptation, integrating the AI supported reading program within established literacy routines while exercising professional judgment. Third, teachers used AI-generated data alongside classroom observations and professional expertise to inform instructional decisions, while recognizing that students differed in their readiness to benefit from AI-supported instruction. Fourth, participants identified classroom organization, instructional time, technology reliability, student engagement, and supportive learning environments as important factors influencing successful implementation.

The findings suggest that effective implementation of AI-supported reading programs depends not only on the capabilities of the technology but also on teachers’ ongoing professional learning, instructional decision-making, and the classroom conditions that support meaningful integration into evidence-based literacy instruction. Contributions to the growing literature on artificial intelligence in elementary literacy education were provided with an in-depth understanding of teachers’ experiences implementing AI supported reading technology in authentic classroom settings.

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