La revolución tecnológica ha transformado profundamente nuestra sociedad y, como consecuencia, los procesos de enseñanza-aprendizaje. Las nuevas generaciones, inmersas en un entorno digital desde su nacimiento, presentan características distintivas en la forma de acceder, procesar y construir conocimiento. La inteligencia artificial (IA) ha emergido como un actor fundamental en esta transformación, redefiniendo los límites entre el aprendizaje formal e informal y planteando nuevos desafíos y oportunidades para la educación superior.

En este contexto, resulta esencial comprender las características del nuevo aprendiz, cómo la IA está impactando su formación y qué implicaciones tiene esto para las instituciones educativas. Este artículo examina la interrelación entre las nuevas culturas de aprendizaje, la identidad digital de los estudiantes y el papel de la IA en la transformación educativa, con especial énfasis en la educación superior.

Education often struggles to keep pace with other sectors because it prioritizes traditional knowledge rather than forward-looking innovation. While the technology and healthcare industries actively anticipate and adapt to emerging trends, education typically focuses on historical accomplishments. Literature courses continue to highlight classic texts without fully exploring digital storytelling such as interactive fiction, podcasts, or virtual reality narratives. Similarly, mathematics classes emphasize established techniques and old ways of solving problems rather than engaging students with contemporary, real-world applications such as climate modeling, data science, or artificial intelligence.

Por un lado, escuchamos los argumentos del tipo: “la incursión de las herramientas de IA Generativa en las aulas es inevitable”; “los profesores, para hacer el mejor uso de estas tecnologías, tienen que actualizar rápidamente sus conocimientos y convertirse en docentes alfabetizados en IA”; o “es urgente replantearnos cómo las formas y prácticas educativas tradicionales tienen que cambiar y adaptarse a las posibilidades de la IA”. 

Por otro, vemos cómo una vez más damos la bienvenida a la promesa que acompaña los discursos innovadores y disruptivos, y que nos hacen pensar, una vez más, que la IA será la solución de nuestros problemas educativos; priorizamos una vez más los medios frente a los procesos; volvemos a dejarnos maravillar por la magia de una tecnología y nos volvemos a olvidar de los objetivos de aprendizaje, de las competencias, del contexto, de las necesidades del alumnado, del aprendizaje autorregulado (Serrano & Sánchez, 2024).

For decades, Learning and Development (L&D) teams have grappled with four underlying wicked problems: scaling internal expertise, ensuring consistency across global teams, keeping up with rapid industry changes, and demonstrating ROI. While some companies are distracted by using AI to optimise for efficiency and cost savings, others are using AI to solve wicked problems that have previously been difficult—or even impossible—to solve at scale.

One thing we've noticed on the ground is that, when it comes to AI-driven L&D innovation, professional services firms - e.g the Big Four - are leading the way, emerging as both early adopters and standard-setters for other orgs in all things AI & L&D. The question we have been exploring recently is: why is this?

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.

Uas universidades a menudo carecen de flexibilidad suficiente para ajustar rápidamente sus planes académicos. Las tecnologías, y todas las áreas del conocimiento que hacen uso de ellas, evolucionan más rápidamente que los planes de estudios de nivel superior (técnico-tecnológicos o universitarios de tercer o cuarto nivel). Ante este panorama, las microcredenciales aparecen como un puente que promete actualizar habilidades profesionales con rapidez y precisión.

Higher Education institutions are trying to keep up with an increasingly unpredictable world. It is imperative that we shore up the most foundational cybersecurity and privacy practices: honoring end users’ agency, fostering trust both within the academic community and beyond, centering transparency in our work to the highest extent possible, and involving all stakeholders in the daily work and responsibility of cybersecurity and privacy.

Our expert panel was charged with envisioning the preferred future of supporting agency, trust, transparency, and involvement in higher education and creating an action plan toward that future.

No, not “ChatGPT, write me an essay that will get an A+ on why Lady Macbeth is Shakespeare’s most interesting villain.”

While they’ve undoubtedly complicated the world of plagiarism in the 21st century, ChatGPT and other natural-language AI assistants have also shown they're capable of being very powerful, legitimate study aids.

The difference between getting AI to do your study for you (bad) and getting it to help you study more effectively (good) is all in the prompts.

Prompts are the natural-language (as opposed to computer language) instructions that tell the AI what we want it to do. As with any form of communication, the more precise and specific the instructions that we use, the more likely we will get the results we want.

It has been just over two years since the public was introduced to OpenAI’s ChatGPT (Generative Pre-Trained Transformer). Since that seemingly innocuous day in November of 2022, the world has hit the gas pedal moving in every direction as the age of exploration has become available to all. In doing so, it is leaving large groups of skeptics, cynics, optimists, and opportunists in its wake. While many are moving as fast as possible to implement new technology, there is an equal number encouraging us to proceed with caution as there are still many unknowns. Answers to the questions of what AI is capable of and what can and should be controlled are still blurry at best. 

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?