From Empirical Reflection to Intelligent Diagnosis: Generative AI-Assisted Pedagogical Reform for Rural Teachers

Abstract

Rural education has long confronted the practical dilemma of a scarcity of professional pedagogical and research support, constraining teachers to superficial, empirical-level reflection. Generative Artificial Intelligence (GenAI) provides a technological pathway to dismantle this bottleneck. Grounded in the TPACK theoretical framework, this conceptual and design-based study deeply integrates GenAI as a digital scaffold throughout rural teachers’ pedagogical optimization. The research constructs an intelligent assistance system comprising data normalization, structured prompting, and differentiated feedback. By reconstructing a pedagogical reform case in elementary mathematics, the study hypothesizes the transformational trajectory from traditional empirical reflection toward intelligent diagnosis. The proposed system aims to identify cognitive disconnections and generate dynamic verification scaffolds to bridge the logical chasm in instructional sequences. To mitigate technological alienation, this study establishes a “Human-in-the-Loop” (HITL) collaborative review principle and proposes safeguarding mechanisms for data privacy and algorithmic dependency. Ultimately, this study suggests the feasibility of transitioning to data-driven paradigms, providing theoretical support for enhancing rural teachers’ competencies.