Methodology for Personalizing Media Education Based on Digital Pedagogy and Artificial Intelligence

Аннотация

In the era of rapid digital transformation, © 2025 Original content from this work may be used under the terms of the creative commons attributes 4.0 License. media education must evolve to address the challenges and opportunities presented by an information-rich, technology-driven society. The integration of digital pedagogy and artificial intelligence (AI) has the potential to fundamentally personalize media education, enhancing individual learner engagement, motivation, and academic success. This article explores the theoretical foundations and practical strategies for personalizing media education through digital pedagogy, augmented by AI-driven approaches. The study draws upon an extensive review of contemporary literature, analysis of AI-powered educational technologies, and observation of emerging best practices in digital learning environments. It is argued that a methodological framework centered on personalization requires a deep synthesis of pedagogical theory, technological design, and ethical considerations. The findings highlight the transformative role of adaptive learning algorithms, data analytics, and intelligent tutoring systems in tailoring content, feedback, and pacing to individual needs. Keywords: Media education, personalization, digital pedagogy, artificial intelligence, adaptive learning, educational technology, individualized instruction. Introduction: The digital revolution has fundamentally altered the nature of media, learning, and communication. In this dynamic landscape, the field of media education must respond not only to the proliferation of information but also to the profound European International Journal of Pedagogics 8 https://eipublication.com/index.php/eijp European International Journal of Pedagogics shift in how individuals access, interpret, and create the intersection of digital pedagogy principles and AI media content. Traditional, one-size-fits-all functionalities. This entailed analyzing models of approaches to education are increasingly unable to adaptive learning, learner profiling, formative meet the diverse needs and preferences of assessment, and feedback loops, and situating them contemporary learners. As such, there is a growing within broader discourses on personalized education impetus to develop pedagogical models that leverage and media competence. Ethical considerations were digital technologies and artificial intelligence (AI) to foregrounded in the analysis, particularly with regard to personalize educational experiences. data privacy, algorithmic transparency, and the role of teachers in AI-mediated learning environments. Personalization in media education entails tailoring content, learning paths, assessments, and feedback to The findings of the study reveal that the effective the unique needs, interests, and abilities of each personalization of media education through digital student. Digital pedagogy provides the theoretical and pedagogy and artificial intelligence depends on several practical foundation for this approach, integrating interconnected factors. At the core of this methodology educational theory with technological innovation. is the dynamic interplay between data-driven decision- Artificial intelligence, as a rapidly advancing field, making, learner agency, and pedagogical intentionality. offers powerful tools for analyzing learner data, AI systems in media education commonly utilize a range adapting instructional strategies, and delivering of data sources, including learner behavior, dynamic, responsive educational experiences. The performance analytics, interaction histories, and self- convergence of these domains signals a paradigm shift reported preferences, to construct detailed learner in media education, necessitating new methodologies profiles. These profiles inform adaptive algorithms that that are both technologically robust and pedagogically adjust content sequencing, media complexity, pacing, sound. and types of assessment in real time. This article seeks to critically examine the methodology One of the primary outcomes of integrating AI with for personalizing media education based on digital digital pedagogy is the creation of individualized pedagogy and artificial intelligence. It aims to learning pathways. In contrast to static, linear curricula, articulate a coherent framework for implementation, AI-driven systems generate dynamic routes through assess the opportunities and challenges inherent in AI- media content, allowing students to explore topics at driven personalization, and offer recommendations for their own pace, revisit challenging concepts, and educators, policymakers, and researchers engaged in accelerate through material they have mastered. This the design and delivery of future-ready media adaptivity is reinforced by intelligent feedback education. mechanisms, which provide timely, specific, and actionable guidance to learners, often in multimodal This research adopts a multi-method approach, format