Micro-Dissatisfaction Accumulation: How Small AI Frustrations Shape Major Pedagogical Concept Changes

This conceptual dissertation elaborates on and refines the Micro-Dissatisfaction Accumulation Model (MDAM), a theoretical framework that redefines the significance of frustration in the integration of artificial intelligence into teacher education. Unlike traditional models that focus on ease of use and seamless integration, MDAM contends that small, recurring micro-frustrations act as cognitive triggers for creativity, innovation, and pedagogical evolution. The dissertation is divided into six structured chapters, synthesizing existing literature on technology adoption, frustration tolerance, cognitive dissonance, and creativity to propose a five-stage process: micro-irritation encounters, frustration accumulation, threshold breakthroughs, creative reconfiguration, and conceptual solidification. Applications in real teaching scenarios reveal how minor AI-related frustrations can stimulate assumption examination, systemic thinking, and identity reconstruction among educators. This framework has profound implications for teacher education programs, professional development, AI tool design, and institutional leadership. The study concludes that educational contexts should not seek to eliminate all frustrations but should strategically manage them to enhance resilience, creativity, and sustainable pedagogical change.