Research article | Open Access
AI Research in Educational Leadership 2026, Vol. 2(1) 1-13
Conditions Shaping AI Implementation in K–12 Schools: A Case Study of Khanmigo
pp. 1 - 13 | DOI: https://doi.org/10.5281/zenodo.20546583
Publish Date: June 04, 2026 | Single/Total View: 0/0 | Single/Total Download: 0/0
Abstract
Grounded in the Technology Acceptance Model (TAM), this qualitative case study examines how K–12 teachers and administrators perceive and implement Khanmigo, an artificial intelligence tool, within their instructional practice. Drawing on multiple data sources and thematic analysis, the study explores how perceived usefulness, ease of use, and organizational conditions shape adoption. Findings reveal that educators’ engagement with Khanmigo is driven by instructional alignment and efficiency, yet mediated by leadership guidance, professional learning, and policy clarity. This study contributes insight to emerging AI-in-K–12 scholarship and offers policy-relevant implications for district-level AI governance and implementation frameworks.
Keywords: Artificial intelligence, Khanmigo, K–12 education, Technology acceptance model, Qualitative case study, Educational policy
APA 7th edition
Egure, D., Curry, K., Olsen, J., & Afolabi, K.M. (2026). Conditions Shaping AI Implementation in K–12 Schools: A Case Study of Khanmigo. AI Research in Educational Leadership, 2(1), 1-13. https://doi.org/10.5281/zenodo.20546583
Harvard
Egure, D., Curry, K., Olsen, J. and Afolabi, K. (2026). Conditions Shaping AI Implementation in K–12 Schools: A Case Study of Khanmigo. AI Research in Educational Leadership, 2(1), pp. 1-13.
Chicago 16th edition
Egure, Dominic, Katherine Curry, Jentre Olsen and Kolawole Michael Afolabi (2026). "Conditions Shaping AI Implementation in K–12 Schools: A Case Study of Khanmigo". AI Research in Educational Leadership 2 (1):1-13. https://doi.org/10.5281/zenodo.20546583
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