Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.

Digital ecosystems are a set of interconnected elements that enable an integrated and seamless digital experience. In education, the use of Artificial Intelligence (AI) has great potential to improve teaching and learning. However, for the expectations placed on the educational use of AI to be met, it is necessary to develop adequate digital ecosystems that allow its effective implementation. Therefore, it is of great importance to deepen the understanding of these ecosystems and their key elements for such implementation. For this purpose, a systematic review of the literature on this subject was conducted, which included the analysis of 76 articles published in peer-reviewed journals. The main results of the review highlight the current focus of research in that matter, which relates digital ecosystems and artificial intelligence around the personalization of learning. Also, some aspects related to this relationship are analyzed from four categories: networks, applications, services, and users.

This article focuses on a review of both literature and practical experiences concerning MOOCs. The literature analyzed was published in peer-reviewed journals between 2007 and 2013. 268 items were selected for this study, of which 100 were analyzed in detail. The issues raised by this analysis were used as the criteria for the analysis of 10 current empirical MOOC experiences. The literature study highlighted the rapid growth in interest in understanding MOOCs and seeking to understand the pedagogic frameworks most relevant to their adoption and the importance of the concept of openness embodied within them. More recently a new emphasis has been emerging where institutional factors, particularly those concerned with financial viability, certification and retention have been highlighted. The analysis of current practice showed that many of the concerns in the academic literature were absent from not only the practices embodied in current MOOC-based learning experiences but seem to have been ignored in the conceptual phase of implementing a MOOC-based teaching model. In practice therefore, most of the current MOOC offer is only a pale reflection of the conceptualization that gave them rise and has been shown to be significant in the literature. In particular the true essence encapsulated in the concept described as Openness has been largely lost in practice.Este artículo se enfoca en una revisión tanto de literatura como de experiencias prácticas acerca de los MOOC. Los textos analizados fueron publicados en revistas entre los años 2007 y 2013. Se seleccionaron 268 artículos para este estudio, de los cuales 100 se analizaron en detalle. Los asuntos encontrados en la revisión se utilizaron posteriormente como criterios de análisis de 10 experiencias empíricas sobre MOOC. La literatura estudiada resalta el rápido crecimiento en el interés por comprender los MOOC, sus fundamentos pedagógicos así como la importancia del concepto de lo abierto que se encuentra en ellos. Un nuevo énfasis ha surgido recientemente en la literatura donde los factores institucionales, particularmente aquellos concernientes con la viabilidad financiera, la certificación y la deserción se encuentran resaltados. El análisis de la prácticas actuales muestra que muchos de los temas relevantes expresados en la literatura académica están ausentes no solo de las prácticas relacionadas con las experiencias de aprendizaje basadas en los MOOC sino que se han ignorado como sustento de la implementación de un modelo de enseñanza basada en ellos. Del análisis realizado se concluye que buena parte de la actual oferta de MOOC es tan solo un pálido reflejo de la conceptualización que les dio origen y que se muestra significativa en la literatura. En síntesis, la verdadera esencia del concepto de lo abierto se ha perdido en la práctica.

Personalized learning (PL) has emerged as a promising approach to address diverse educational needs, with artificial intelligence (AI) playing an increasingly pivotal role in its implementation. This systematic literature review examines the landscape of PL across various educational contexts, focusing on the use of AI and associated challenges. Using the PRISMA guidelines, 68 empirical studies published between 2018 and 2024 were analyzed, revealing correlations between academic levels, learning modalities, technologies, and implementation barriers. Key findings include (a) predominant use of AI in higher education PL implementations, (b) preference for blended learning in secondary and elementary education, (c) shift from technological to pedagogical barriers across educational levels, and (d) persistent psychological barriers across all contexts. This review provides valuable insights for educators, policymakers, and researchers, offering a comprehensive understanding of the current state and future directions of AI-driven personalized learning.