AI literacies are becoming increasingly essential as AI continues to evolve and integrate into a multitude of educational contexts. Rather than viewing literacy as a singular skill, the Dimensions of AI Literacies embrace a pluralistic perspective, recognizing that individuals can develop and apply a range of interconnected competencies. AI literacies encompass the skills needed to comprehend, utilize, and critically evaluate AI within complex environments. As educators, learners, and leaders engage with AI, they must navigate both the technical aspects of AI tools and the ethical, cultural, and social implications of their use. AI literacies enable individuals to make informed decisions, adapt AI tools to different contexts, and ensure that applications of AI in academic settings are ethical and inclusive. The Dimensions of AI Literacies (presented below) offer a flexible and transformative lens for understanding teaching and learning practices in an AI-enabled world. We invite you to explore this taxonomy to reflect on the challenges and opportunities presented by AI in education, leveraging a common vocabulary to identify and share effective practices for meaningfully implementing AI-supported approaches into the classroom.

La Universidad Holoneural redefine el paradigma educativo al integrar la inteligencia distribuida de la Universidad Neural con la hiperrealidad inmersiva de la Universidad Holograma. En este nuevo ecosistema, el aprendizaje no es lineal ni centralizado, sino polimórfico, adaptándose en tiempo real a la interacción cognitiva de cada estudiante mediante meta-algoritmos adaptativos.

Cada aprendiz se conecta a un campo neurodigital expandido, donde la educación se experimenta como una hiperrealidad cuántica, fusionando datos, experiencias y emociones en un flujo continuo de conocimiento. La didáctica tradicional se disuelve en una morfodinámica cognitiva, donde los agentes de IA-AGI funcionan como holo-mentores capaces de simular debates, construir hipótesis en tiempo real y evaluar el impacto de cada idea en un entorno de aprendizaje fractal.

Nos adentramos en la Universidad-Holograma como un circuito vibrante de conocimiento donde las ideas y los aprendizajes no tienen fronteras. En lugar de paredes y aulas, hay flujos de información que se desplazan a través de espacios cuánticos: el aprendizaje no se da en un lugar fijo, sino en un paisaje infinito de datos que puede ser accesible desde cualquier punto del universo digital.

Este holograma epistémico es un campo de interacción gravitacional donde las ideas se entrelazan y se mutan constantemente. El tiempo académico no es lineal ni secuencial; en cambio, se convierte en una red de superposiciones temporales que permiten a los conocimientos del pasado, presente y futuro coexistir en una danza perpetua de emergencias y transformaciones.

Este informe, coordinado por Juanmi Muñoz, Neus Lorenzo, Xavier Suñé y Miquel Àngel Prats, explora desde múltiples perspectivas el impacto transformador de la IA en los procesos educativos. A través de 28 artículos redactados por docentes, investigadores y expertos en innovación educativa de diversas partes del mundo, este informe aborda tanto los fundamentos teóricos como las aplicaciones prácticas, las experiencias en distintos niveles educativos y las implicaciones sociales, emocionales y éticas de la integración de la IA en la educación.

El futuro de la educación se encuentra en una encrucijada, una donde la inteligencia artificial (IA) no solo promete transformar los procesos de enseñanza y aprendizaje, sino redefinir el mismo concepto de inclusividad en los entornos educativos. En este nuevo escenario, los trabajos de investigación acuñados por Juan Domingo Farnós y las intervenciones de expertos como José Blas García e Ingrid Mosquera, desde el  Claustro Virtual, se erigen como faros guía para una educación disruptiva centrada en la inteligencia adaptativa y el aprendizaje inclusivo. Este futuro no se construye en torno a la mera integración de la tecnología, sino en la fusión dinámica entre la IA, la neurociencia educativa y los principios fundamentales de la inclusión emocional y cognitiva. Como nos recuerda Farnós en sus más recientes investigaciones (La IA como plataforma de aprendizaje inclusivo, 1991), la educación debe evolucionar hacia un entorno inclusivo, donde cada estudiante no solo tenga acceso a los contenidos, sino que sea capaz de personalizar su camino en función de sus necesidades emocionales y cognitivas.

A study by Michael Gerlich at SBS Swiss Business School has found that increased reliance on Artificial Intelligence (AI) tools is linked to diminished critical thinking abilities. It points to cognitive offloading as a primary driver of the decline.

AI's influence is growing fast. A quick search of AI-related science stories reveals how fundamental a tool it has become. Thousands of AI-assisted, AI-supported and AI-driven analyses and decision-making tools help scientists improve their research.

The future belongs to curious, passionate individuals unafraid to take intellectual risks, not to those who are passively obedient and skilled at following instructions. Society rewards creativity, innovation, and a willingness to embrace uncertainty, not compliance. Those best positioned for tomorrow are nerds, geeks, makers, dreamers, and knowmads: individuals skilled in navigating complexity, exploring new ideas, and translating knowledge into meaningful action.

A hint to the future arrived quietly over the weekend. For a long time, I've been discussing two parallel revolutions in AI: the rise of autonomous agents and the emergence of powerful Reasoners since OpenAI's o1 was launched. These two threads have finally converged into something really impressive - AI systems that can conduct research with the depth and nuance of human experts, but at machine speed. OpenAI's Deep Research demonstrates this convergence and gives us a sense of what the future might be. But to understand why this matters, we need to start with the building blocks: Reasoners and agents.

De acuerdo con una encuesta realizada entre agosto y octubre de 2024, el 88 % de los estudiantes de licenciatura encuestados del Tecnológico de Monterrey, Campus Guadalajara, reportó mejoras académicas tras el uso de IA Generativa. Entre los beneficios mencionados, se encuentran la mejora en la escritura de ensayos y presentaciones, la creación de resúmenes, la resolución de problemas matemáticos complejos y la preparación para los exámenes. Sin embargo, el 30 % indicó que ha reducido significativamente el tiempo empleado en tareas complejas. Esta información sugiere que muchos estudiantes recurren a la IA no solo como una herramienta de apoyo, sino como un recurso clave para automatizar procesos de aprendizaje, como la generación de predicciones o la síntesis de información extensa en resúmenes de una página. Esta tendencia plantea preguntas sobre la dependencia tecnológica y sus efectos en el pensamiento crítico, la autonomía intelectual y la equidad educativa.

In a world defined by rapid advances in science, technology, and the very structure of work, a university degree can become obsolete faster than a banana browns on your kitchen counter. The old paradigm of “earn your diploma, land a career, follow a predictable path” no longer makes sense in what many call an Age of Disruption. What we value at the moment as useful knowledge, once stable enough to organize into four-year degree programs, now evolves so quickly that much of what students learn in their freshman year is outdated before they step into their sophomore classrooms.

Our mainstream education systems are obsessed with measurement, but they measure the wrong things. Schools track test scores, attendance, and graduation rates as if these numbers define learning. They do not. The focus on high-stakes testing does not improve education; it distorts it. Students are taught to perform rather than to understand. Teachers are pressured to cover material instead of cultivating curiosity and creativity. Policymakers equate test results with progress while ignoring whether students can think critically, solve problems, or apply knowledge in real-world contexts.

The Digital Education Council Global AI Student Survey aims to provide insights into student perceptions around artificial intelligence (AI) in higher education to inform decision-making for university leadership. The report covers the status of AI usage and readiness, student perception of AI use cases, expectations and preferences on university actions on AI, satisfaction with institutions’ AI adoption, concerns and key attributes for AI use.

This survey has gathered 3839 responses across 16 countries, from bachelor, masters, and doctorate students in multiple fields of study, offering a diverse range of student viewpoints on AI in education. With universities both keen to integrate AI into their institutions and wary of possible liabilities and concerns
that this integration may bring, this survey aims to be a guide to take action.

Data from this survey provides valuable guiding points for universities on general student sentiment, brings up notable useful AI use cases that universities can implement and highlights the largest areas of concern amongst students that universities should consider. The varied and global perspectives represented in the Global AI Student Survey would not have been possible without the support and contributions of DEC institution members and all universities who assisted in the distribution of this survey.

Over the past three weeks, we have explored if and how AI can enhance Analysis, Design, Development and Implementation. This week, we turned our focus to if and how AI can make the evaluation part of process not just faster but also more effective.

Through hands-on experiments, we found that AI has the potential both the accelerate and augment the evaluation part of ADDIE. Specifically, we found four areas where existing AI tools can make us both faster and better at evaluation:

La educación tiene como objetivos la transmisión de la cultura de una sociedad y el desarrollo personal de quien aprende; la tecnología es una industria que cotiza en bolsa y responde ante sus consejos de dirección y sus inversores acerca de sus logros y sus problemas.

The rapid emergence of agentic AI has forced the learning and development field to confront a long-standing truth: most asynchronous online learning is not well designed.

Last week, I analyzed how AI-driven agents like Manus are autonomously completing traditional online courses—moving through static content, passing quizzes and even meaningfully contributing to online discussions.

The point I put forward was that the problem is not AI's ability to complete online async courses, but that online async courses courses deliver so little value to our learners that they delegate their completion to AI.

The harsh reality is that this is not an AI problem — it is a learning design problem.

However, this realization presents us with an opportunity which we overall seem keen to embrace. Rather than seeking out ways to block AI agents, we seem largely to agree that we should use this as a moment to reimagine online async learning itself.

In Online Education and training, professionals rightfully prioritize learners—but too often, they lack meaningful opportunities for their own professional development. Throughout my work with various organizations, I have seen training efforts focus primarily on mastering new technologies or attending “train-the-trainer” programs that equip educators with specific content or skills to pass on to others. While these are valuable, they only address part of the equation. True professional growth must go beyond tools and instructional techniques. It must focus on pedagogy—the art and science of teaching—so that online educators and trainers can strengthen their ability to engage learners, adapt to different learning needs, and create truly effective learning experiences.

Education Systems that limit creativity perpetuate inequity. Defining success only in terms of conformity and compliance leaves out students whose strengths include innovation, originality, or unique thinking. Such restrictive approaches marginalize learners who approach problems differently or express themselves in different ways. True equity means recognizing and valuing a diversity of talents, ideas, and experiences, intentionally dismantling systemic barriers that restrict creative expression and innovative thought.

When technology fails in education, we blame the tools. The platform is outdated. The AI tutor doesn’t work. Students are distracted by their devices. But these failures are not the root cause of the problem; they are symptoms of a deeper dysfunction in how we approach learning.

If you work in L&D, you've likely seen the viral videos this week: Manus and other agentic AI tools completing asynchronous training courses in minutes. These AI agents don’t just assist learners, they autonomously complete courses by responding to questions, submitting assignments and even contributing meaningfully to online discussions.

Earlier this week, OpenAI announced “a first-of-its-kind consortium with 15 leading research institutions dedicated to using AI to accelerate research breakthroughs and transform education.” Like the press release last month announcing its partnership with California State University, I see this as an act of desperation by a company that has yet to figure out a profitable strategy for selling what it hopes is an educational technology. If that turns out to be the case, and, boy howdy, there are a lot of people hyping that idea, they want to sell their product directly to institutions.

I’ve had many conversations recently with colleagues about what happens when we integrate GenAI into our daily work. What effects does it have on our cognition? What do we gain and what do we lose in this process? Does using Claude or ChatGPT to help with writing atrophy certain parts of our brain – the parts having to do with crafting an argument, finding the right structure and words to convey your point, and perhaps most importantly, thinking deeply about what we want to say?

La finalidad de cualquier formación e-learning es que los alumnos puedan avanzar por el temario con agrado y agilidad.

Así, en el proceso de creación hay una etapa previa en la que el profesorado debe decidir cuál es la mejor estructura y metodología para presentar los contenidos.

Pues bien, es en ese momento cuando entra en juego lo que se conoce como diseño instruccional en eLearning. Sin duda, un proceso imprescindible para que un curso online tenga calidad y una buena base pedagógica.

A lo largo de este post, te contamos qué es, las fases comunes en la mayoría de los modelos y las técnicas para ponerlo en marcha.

Education often mistakes knowledge for information, reducing learning to the storage and recall of facts. Students are tested on their ability to recall information, not on their depth of understanding. However, memorization is not the same as knowledge. Learning is a process of transformation, not just accumulation. To make sense of the world, students must move beyond collecting data and information to constructing knowledge. True innovation happens only when they take what they know and use it to create new meaning and value. Understanding these distinctions reveals a core flaw in education. Schools excel at managing information, but they cannot manage knowledge without reducing it back into information.

As AI’s prevalence continues to grow in higher education, many decision-makers agree: We’ll look back on the AI debate much like we do concerns about calculators in math classrooms. Students still need foundational skills, but once those are mastered, tools like calculators and AI become essential additions. AI is here to stay, and institutions are rapidly shifting from asking “How do we ban AI?” to “How do we embrace AI and empower our students to use these tools responsibly?”

El Dr. Ruben Puentedura creó en el 2010 un modelo estratégico para ayudar a docentes a visualizar la integración de tecnologías en los planes de estudio, llamado SAMR; donde cada letra va ascendiendo en la complejidad del uso de la herramienta y todas puntualiza las diferentes etapas de la incorporación de las herramientas de tecnología educativa (también conocidas como EdTech): Sustitución, Aumento, Modificación y Redefinición.