The TPL-DC Model: Generative AI–Enhanced Personalized Training for Teachers’ Digital Competency

Abstract

The rapid advancement of generative artificial intelligence (AI) has intensified the need for teaching professional development models that move beyond tool-level training toward adaptive, competency-oriented learning environments. This study aims to design, develop, and validate the TPL-DC Model (Training Personalized Learning for Digital Competency Development), a design-oriented framework that integrates structured training processes, personalized learning principles, and generative AI-enhanced instructional mechanisms to support future-ready teacher digital competency development. Employing a research and development (R&D) methodology, the study synthesizes theoretical foundations across four domains: training learning systems, personalized learning, generative AI in training, and digital competency frameworks. The synthesis resulted in a unified seven-stage Training Personalized Learning Process, within which generative AI functions, such as learner analytics, adaptive content generation, intelligent recommendations, and continuous monitoring, are systematically embedded across all stages of the training cycle. The model operationalizes digital competency development across five core domains: information and data literacy, digital content creation, communication and collaboration with AI, problem solving and innovation, and safety and ethics. Model validation was conducted through expert evaluation involving seven specialists in educational technology and AI in education. The results indicate a high level of conceptual coherence, structural appropriateness, and practical feasibility, with an overall mean rating of 4.88 out of 5.00. The findings suggest that the TPL-DC Model provides a theoretically grounded, scalable blueprint for designing AI-enhanced teacher-training environments. Rather than focusing on effectiveness testing, this study contributes a process-level, AI-embedded training architecture that advances personalized, data-driven, and sustainable teacher professional learning in AI-mediated educational ecosystems.