The AI-STORY Framework: A Conceptual Evaluative Model for Integrating Generative AI into Rural Teacher Professional Development
Keywords:
Instructional Design, Teacher Professional Development, AI-STORY Framework, Generative Artificial Intelligence, Rural SchoolsAbstract
Background and Objectives: This article synthesizes knowledge on integrating Generative Artificial Intelligence (GenAI) into rural teacher professional development. It identifies research gaps and proposes the AI-STORY framework as a conceptual evaluative model for examining whether GenAI integration aligns with educational contexts, curricula, ethical standards, classroom practice, and evidence of learning.
Methods: A targeted literature review and conceptual synthesis were conducted across five domains: GenAI in education, teacher professional development, rural education, AI literacy, and AI ethics. Evidence was stratified into three layers and mapped against seven AI-STORY dimensions: Analyze, Identify, Scaffold, Transform, Optimize, Run, and Yield.
Results: Current research is relatively strong in conceptual design, learning-objective identification, teacher readiness, and contextual analysis of rural settings. However, significant gaps remain in classroom implementation of AI-generated resources and empirical evidence of learner-level outcomes. Much of the existing literature still emphasizes teacher attitudes, readiness, and tool use rather than demonstrated pedagogical impact.
Conclusion: The AI-STORY framework is proposed as a conceptual evaluative model for examining the quality of GenAI integration in rural teacher professional development. Rather than functioning as a procedural checklist, it helps educators and policymakers consider contextual fit, teacher support, ethical review, classroom enactment, and evidence of learning before making claims about the educational value of GenAI.
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