Afri Yudantoko, Kurniawan Sigit Wahyudi, Nabila Naila Fatin, Sisca Rahmadonna, Muhammad Ihsaan Fathoni, Rita Fransina Maruanaya
Vocational teacher preparation programs require coherent integration of pedagogical competencies, technical competencies, and authentic instructional technology in line with professional practices. This research aims to develop and refine the didactics design principles of an AI-Integrated Metacognition-Based Microlearning Platform. This research uses a Design-Based Research (DBR) approach, with Focus Group Discussion (FGD) as the main element, across three iterative stages: analysis and exploration, design and construction, and evaluation and reflection. This FGD activity involved six experts from the fields of vocational learning, instructional media and technology, and educational psychology to identify important vocational learning activities, instructional technology requirements for microlearning integrated with AI technology, and learning strategies that facilitate students' metacognitive thinking skills. The results of this study indicate that the vocational teacher preparation program requires learning that balances the content of technical competencies and pedagogical competencies supported by instructional technology and learning resources that are valid, structured, and contextual. Furthermore, the microlearning-based approach will be effective if implemented with a clear sequence, appropriate duration, and alignment with the cognitive and professional characteristics of the learners. The integration of Artificial Intelligence (AI) features, such as learning analytics, adaptive content, and feedback, is indispensable for strengthening learners' iterative thinking and reflection. In addition, metacognitive learning strategies, such as self-monitoring, reflective questioning, feedback, and collaborative learning activities, can improve learners' ability to learn how to learn. In conclusion, three elements; Artificial Intelligence, Metacognition, and Microlearning work together to support learning-how-to-learn activities in which artificial intelligence personalizes learning, metacognition helps learners plan and evaluate their understanding, and microlearning provides short, flexible learning units that enable continuous self-regulated learning. Overall, this research produces empirical design principles to support vocational teacher preparation programs that are meaningful, sustainable, and aligned with the demands of the world of work. Copyright (c) 2026 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/
Department of Automotive Engineering Education, Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Department of Educational Technology, Faculty of Education, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Department of Vocational Education and Vocational Didactics, Faculty of Education, Technische Universität Dresden, Dresden, Germany
