This study introduces a methodological framework for instructor-designed generative AI in dental hygiene education, aiming to establish a theoretical structure that supports self-regulated learning (SRL) through deliberate instructional design and processoriented analysis. By synthesizing literature on generative AI, prompt engineering, SRL theory, and process mining, this paper outlines design principles for AI-integrated learning protocols. The proposed framework incorporates three core components: instructor-guided prompt and protocol principles aligned with SRL phases, a structured interaction log schema for documenting learner–AI exchanges, and a process mining-informed model for mapping learning pathways. Through these components, the study seeks to reduce educational disparities in the AI era and improve the quality of dental hygiene education by enabling datadriven feedback and fostering self-regulated learning.