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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.

Despite reaching intermediate English proficiency, many bilingual secondary students in Colombia struggle with academic writing due to difficulties in organizing ideas and expressing arguments coherently. To address this issue, this study explores the integration of AI-powered tools—Grammarly and ChatGPT—within the Writing Workshop Instructional Model (WWIM) to enhance students’ writing skills. Conducted at a bilingual secondary school, the intervention targeted 10th grade ESL learners and focused on improving grammar accuracy, textual coherence, and organizational structure. Drawing on Galbraith’s model of writing as content generation, the study adopted a design-based research methodology, incorporating iterations of implementation, feedback, and refinement. The results indicate that combining WWIM with AI feedback significantly improved students’ academic writing performance. Learners reported greater confidence and engagement when revising drafts using automated suggestions. These findings highlight the pedagogical potential of integrating AI tools into writing instructions and offer practical implications for enhancing academic writing curricula in secondary ESL contexts.

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.

Los tuits o mensajes publicados en la red social Twitter son entendidos como huellas digitales que se producen por la interacción de las personas en entornos digitales. Estas huellas se generan tanto en los procesos de educación formal como los que se conducen a través de Ambientes Virtuales de Aprendizaje, como en los procesos de interacción social propios de las redes sociales. En este estudio se analizaron los procesos de representación y aprendizaje de conceptos a partir de la producción de tuits generados por tres grupos de estudiantes universitarios de tercer y quinto año. Dicho estudio de carácter mixto se adelantó bajo el enfoque del aprendizaje supervisado que contempló dos momentos: uno de instrucción y otro de evaluación. Los tuits se analizaron desde tres categorías: contenido, contenedor y contexto, así como desde las operaciones intelectuales del pensamiento conceptual: supra-ordinación, exclusión, infra-ordinación e iso-ordinación. Adicionalmente, se analizó el tono emocional de los tuits mediante técnicas de análisis de contenido, minería de textos y de análisis de sentimiento. Los resultados del estudio señalan la posibilidad de que las huellas digitales puedan ser utilizadas como indicadores de los procesos de representación y aprendizaje de conceptos, no solo desde la perspectiva de la construcción lingüística y cognitiva que supone aprender y representar conceptos, sino desde las condiciones emocionales que se dan en las interacciones de una red social como Twitter. A partir de allí se discuten y se abordan conclusiones relacionadas con el potencial transformador del uso de huellas digitales en educación.

The integration of Generative AI (GAI) in education has opened new possibilities for personalized learning, yet its effectiveness in mitigating learning gaps remains underexplored. This study examines the impact of Personalized Learning Pathways (PLPs), generated through AI models (Gemini 2.5 Pro, ChatGPT 5), on secondary school students’ learning outcomes. Using a short-term longitudinal panel design, the research compares homogeneous instructional strategies with AI-driven personalized learning to assess differences in knowledge acquisition and cognitive skill development. Findings indicate that AI-generated PLPs significantly reduce lower-order learning gaps, though higher-order skills remain challenging. The study also reveals that learning styles influence student engagement with AI-driven education, suggesting that hybrid models combining AI and teacher mediation may optimize outcomes. These findings contribute to the ongoing discourse on AI in education, emphasizing the need for equitable, adaptive, and ethical AI applications in learning environments.

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.

The world is fast becoming increasingly digital, networked, and mobile. The use of mobile devices is a growing educational trend and determines how knowledge is taught and used when teaching and learning. This article presents the results of a comparative analysis of web and mobile educational content, which focuses on instructional issues that affect learning in a mobile context—namely, length, density, complexity, purpose, and structure. It then demonstrates that mobile content is shorter, denser, and more complex than the content of other types of educational media, and it proposes a critical assessment of how such content should be designed.

Artificial intelligence is revolutionizing education, especially in language learning. This mixed method study examines how an AI-based application can improve the oral expression in English of 40 fifth-grade primary school students. Using the Design-Based Research (DBR) methodology, an AI-powered APP was developed and implemented over 16 weeks to support the development of oral English skills in the participating students. The results showed: a) a significant increase in student participation and motivation, b) notable improvements in pronunciation, fluency, and conversation skills, c) adaptability to the individual needs of students, and d) gradual progress in performance scores. The study highlights the transformative potential of AI in education, offering a personalized and effective learning experience. These findings are valuable for educators, educational technology developers, and policymakers interested in integrating AI into language teaching.

The world is fast becoming increasingly digital, networked, and mobile. The use of mobile devices is a growing educational trend and determines how knowledge is taught and used when teaching and learning. This article presents the results of a comparative analysis of web and mobile educational content, which focuses on instructional issues that affect learning in a mobile context—namely, length, density, complexity, purpose, and structure. It then demonstrates that mobile content is shorter, denser, and more complex than the content of other types of educational media, and it proposes a critical assessment of how such content should be designed.

It may seem that learning platforms and systems are a tired topic for the academic community; however, with the recent advancements in artificial intelligence, they have become relevant to both current and future educational discourse. This systematic literature review explored platforms and software supporting personalized learning processes in the digital age. The review methodology followed PRISMA guidelines, searching Scopus and Web of Science databases. Results identified three main categories: artificial intelligence, platforms/software, and learning systems. Key findings indicate artificial intelligence plays a pivotal role in adaptive, personalized environments by offering individualized content, assessments, and recommendations. Online platforms integrate into blended environments to facilitate personalized learning, retention, and engagement. Learning systems promote student-centered models, highlight hybrid environments’ potential, and apply game elements for motivation. Practical implications include leveraging hybrid models, emphasizing human connections, analyzing student data, and teacher training. Future research directions involve comparative studies, motivational principles, predictive analytics, adaptive technologies, teacher professional development, cost-benefit analyses, ethical frameworks, and diverse learner impacts. Overall, the dynamic interplay between artificial intelligence, learning platforms, and learning systems offers a mosaic of opportunities for the evolution of personalized learning, emphasizing the importance of continuous exploration and refinement in this ever-evolving educational landscape.