David López-Villanueva

The manuscript _Metacognition and Pedagogy in the Era of Artificial Intelligence_ presents a timely and original perspective by positioning metacognition as a key educational response to advances in AI. It effectively links AI milestones (e.g., Deep...

Educational research requires robust methodologies that aid in the design of new pedagogical practices. In this context, Design Science Research (DSR) presents itself as a solid alternative to Design-Based Research (DBR). The main objective of this article is to present DSR as a methodological tool for educational research, highlighting its foundations, variants, and practical applications. Throughout the article, we will conduct a detailed analysis of DSR and its variants through examples of the development of educational technologies, pedagogical innovations, and the evaluation of educational programs. The most notable results show that DSR, with its cyclical structure that includes the relevance cycle, the design and construction cycle, and the rigor cycle, has been successfully applied in the development of online learning platforms, formative assessment tools, and pedagogical models. In comparison, DBR is characterized by its collaborative approach and methodological flexibility, although it presents challenges in replicating results and selecting control groups. The conclusions indicate that DSR offers a structured and rigorous methodology that can improve educational research through the creation and evaluation of resulting products, ensuring greater replicability. On the other hand, DBR is valuable for its practical and collaborative approach; the choice between DSR and DBR will depend on the specific nature of the research and the available resources, suggesting that integrating both methodologies could offer a more comprehensive and effective solution to address educational challenges.

This paper introduces Educational Conceptual Model Design a new research methodology to develop conceptual models in the field of educational technology. It is based on principles from Conceptual Design Methodology, Conceptual Modeling Methodology, Design-Based Research, and Design Science Research. It is a systematic methodology structured in four phases: problem definition, conceptual foundation, model development, and knowledge contribution. This methodology is designed to be streamlined and grounded in theory, focusing on consistency, replicability, and alignment with theoretical rigor. The methodology is particularly suited to respond to rapid advancements in technology and continuously evolving educational challenges, including the integration of emerging tools such as Artificial Intelligence in Education. Its main characteristic is that it offers a transition from problem identification to practical models with a special emphasis on the design process. This approach reduces the complexity of developing a model while maintaining its applicability across different educational technology areas, which can range from adaptive learning systems to data-driven learning analytics. The paper concludes by discussing the methodology’s contributions to theory and practice, its limitations, and potential future directions, including scalability, cross-disciplinary integration, and the incorporation of disruptive technologies. This methodology serves as a foundational framework for researchers and practitioners aiming to design innovative forward-thinking solutions that meet the dynamic needs of modern education.

The recent emergence of Artificial Intelligence (AI) has the potential to influence the teaching-learning process. Some of the most used pedagogical approaches of the last decade have been Flipped Classroom and Flipped Learning. This article explores the intersection between Flipped Learning and AI through qualitative research based on interviews with international experts in the field. The results reveal the significant impact of AI on education, highlighting how AI tools are transforming teaching and learning methodologies. Additionally, the evolution of Flipped Learning with the integration of AI is analyzed, showing how this combination enhances personalized learning and improves student engagement. Finally, the role of the teacher in this new educational paradigm is discussed, emphasizing the need for continuous adaptation and the development of new competencies to fully leverage emerging technologies. With this study, we aim to provide an overview of the opportunities and challenges that AI presents in the context of Flipped Learning.

The potential influence of Artificial Intelligence (AI) is obvious in all the elements of teaching and learning processes. Some of the most used methodological approaches of the last decade have been Flipped Classroom and Flipped Learning. This article explores the intersection between Flipped Learning and AI through qualitative research based on interviews with international experts in the field. The results reveal the significant impact of AI on education, highlighting how AI tools are transforming teaching and learning methodologies. Additionally, the evolution of Flipped Learning with the integration of AI is analyzed, showing how this combination enhances personalized learning and improves student engagement. Finally, the role of the teacher in this new educational paradigm is discussed, emphasizing the need for continuous adaptation and the development of new competencies to fully leverage emerging technologies. With this study, we aim to provide an overview of the opportunities and challenges that AI presents in the context of Flipped Learning.