[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-582324-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-582324-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","the-tpl-dc-model-generative-aienhanced-personalized-training-for-teachersdigital-competency","The TPL-DC Model - Generative AI–Enhanced Personalized Training for Teachers’Digital Competency","","The rapid advancement of generative artificial intelligence (AI) intensifies the need for teaching professional development models that move beyond tool-level instruction toward adaptive, competency-focused learning environments. This research designs, develops, and validates the TPL-DC Model, integrating structured training processes, personalized learning principles, and generative AI–enhanced instructional mechanisms for teacher digital competency growth. Using an R&D approach, it synthesizes four domains and yields a seven-stage training process with AI embedded across analytics, adaptive content, recommendations, and monitoring. Expert validation shows strong conceptual coherence and feasibility.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/the-tpl-dc-model-generative-aienhanced-personalized-training-for-teachersdigital-competency/582324/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/the-tpl-dc-model-generative-aienhanced-personalized-training-for-teachersdigital-competency/582324.png","ImageObject",300,407,{"name":42,"@type":43},"Margaret","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-08","2026-10-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What does the TPL-DC Model aim to achieve for teachers?","Question",{"text":63,"@type":64},"It is designed to support future-ready teacher digital competency development by combining structured training, personalized learning, and generative AI–enhanced instructional mechanisms.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is generative AI embedded in the model?",{"text":68,"@type":64},"Generative AI functions such as learner analytics, adaptive content generation, intelligent recommendations, and continuous monitoring are embedded systematically across the training cycle’s stages.",{"name":70,"@type":61,"acceptedAnswer":71},"How was the TPL-DC Model validated and what were the results?",{"text":72,"@type":64},"Validation used expert evaluation with seven specialists in educational technology and AI in education. The results indicate high conceptual coherence, structural appropriateness, and practical feasibility, with an overall mean rating of 4.88 out of 5.00.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},582324,1792190457,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":55,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":108,"preview_page_count":4},137451207643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","The TPL-DC Model: Generative AI–Enhanced Personalized Training for  \nTeachers’Digital Competency  \nDuangruethai Seewan 1, Rungfa Pasmala2, Tarattakan Pachumwon3, Bhibul Hongthong4,* & Thada Jantakoon5 1The Secondary Educational Service Area Office Phetchabun, Thailand  \n2Department of Information and Communication Technology for Education, Faculty of Industrial Education, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand  \n3Department of Information Technology and Multimedia, Rajamangala University of Technology Isan, Surin Campus, Surin, Thailand  \n4Department of Educational Administration, Faculty of Education, Rajapruk University, Thailand  \n5Department of Information and Communication Technology for Education, Faculty of Science and Technology, Rajabhat Maha Sarakham University, Thailand  \n*Correspondence: Department of Educational Administration, Faculty of Education, Rajapruk University, Thailand. [E-mail: bhibul.hongth@gmail.com](E-mail: bhibul.hongth@gmail.com)  \nReceived: January 8, 2026  \ndoi:10.5430/wje.v16n1p46  \nAccepted: February 26, 2026 Online Published: March 9, 2026 URL: [https://doi.org/10.5430/wje.v16n1p46](https://doi.org/10.5430/wje.v16n1p46)  \nAbstract  \nThe 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.  \nKeywords: teacher training, generative artificial intelligence, personalized learning, digital competency, training model design  \n1. Introduction  \nThe rapid advancement of generative artificial intelligence (AI) is reshaping educational systems worldwide, creating new opportunities and challenges for teacher professional development. As schools increasingly integrate intelligent technologies into instructional practice, teachers are expected to develop advanced digital competencies, including data literacy, AI-mediated communication, adaptive content creation, and ethical technology use (Ng et al., 2023; Mejías-Acosta et al., 2024) . These emerging expectations require training environments that equip ","cbCaibYDm8eWSnTe","https://ap.wps.com/l/cbCaibYDm8eWSnTe","pdf",325387,16,"English","# Introduction\n## Rationale and background\n## Purpose and research gap\n# Abstract\n## Model overview and development approach\n## Validation and results\n# Keywords","[{\"question\":\"What does the TPL-DC Model aim to achieve for teachers?\",\"answer\":\"It is designed to support future-ready teacher digital competency development by combining structured training, personalized learning, and generative AI–enhanced instructional mechanisms.\"},{\"question\":\"How is generative AI embedded in the model?\",\"answer\":\"Generative AI functions such as learner analytics, adaptive content generation, intelligent recommendations, and continuous monitoring are embedded systematically across the training cycle’s stages.\"},{\"question\":\"How was the TPL-DC Model validated and what were the results?\",\"answer\":\"Validation used expert evaluation with seven specialists in educational technology and AI in education. The results indicate high conceptual coherence, structural appropriateness, and practical feasibility, with an overall mean rating of 4.88 out of 5.00.\"}]","The TPL-DC Model - Generative AI–Enhanced Personalized Training for Teachers’Digital Competency | PDF",1792043044]