El más reciente documento de la UNESCO propone cambios trascendentales en el modelo educativo tradicional. De igual manera, plantea serios retos y dilemas socio-tecno-educativos de deben atenderse a la brevedad posible.

Te invito a leer el resumen de este documento y a ver el video-overview que resume los principales plantemientos de la UNESCO sobre el impacto de la IA en la Educación.

La arquitectura de internet atraviesa una metamorfosis que trasciende la mera evolución tecnológica. Lo que algunas voces especializadas denominan Web 3.5 representa, en realidad, la materialización de décadas de convergencia entre sistemas que hasta hace poco operaban en silos diferenciados. Esta fase —más transicional que definitiva— configura el sustrato sobre el que se erigirá una nueva generación de infraestructuras digitales capaz de alterar, de forma estructural, las relaciones entre individuos, organizaciones y sistemas automatizados.

Colleges are now closing at a pace of one per week. What happens to the students?” Jon Marcus asked in a recent Hechinger Report piece. It’s not a rhetorical question — and it doesn’t have an easy answer. As educators, we’ve read the headlines, seen the numbers, and felt the pressure. Undergraduate enrollment is down. Student confidence is eroding. The enrollment cliff looms. 

El punto de partida es incontestable: los «detectores de texto escrito por inteligencia artificial» no son una base válida para castigar a ningún estudiante. La evidencia lo deja claro: la propia OpenAI retiró su «AI Text Classifier« por su bajo índice de acierto, una admisión explícita de que el enfoque no funcionaba como prometía. La razón de fondo es clara: abundantes falsos positivos y gran facilidad para «engañar» a los detectores.

“Artificial Intelligence”. The issue is embedded right in the name: Artificial means not real. Is it here to stay, and can we even fight it? One of our generation’s premier philosophers, Dr. Yuval Harari, said that if we hope to survive, we better fight it (2015). Technology has been hypothesized to be an evolutionary mismatch (Li & Colarelli, 2017). This term implies that behaviors that once supported a species have become injurious. An example of a mismatch is sugary foods. Our nomadic ancestors struggled to procure enough daily calories to sustain life. When they found sugary food, they filled up on it and stored it. In our modern day, too much sugar leads to issues related to early mortality.  

I am not sure that we have quite grasped the enormity of the upheaval that artificial intelligence will bring. The disruption will be orders of magnitude bigger and faster than we have witnessed before, and we won’t have a generation or two to adapt as we had in the industrial revolution. So the question for a place like Oxford is not whether we have Blake’s ‘bow of burning gold’ to fight against AI, but whether we’re up for the challenge – internally and externally – to help humanity adapt, at pace, and to help shape AI into a trusted ally so that human and artificial intelligence co-create a more just, hopeful and kinder society. Building a new story for humans and humanity. And, while I fully recognize the anxiety this new world brings, I say we must get ready.

AI has renewed debate about learning, memory, and creativity in an age of cognitive offloading. Cognitive offloading means using tools to do mental work we could do ourselves. Many ask if students who use AI to draft essays, solve problems, or recall facts will stop building core skills. These concerns echo older worries about writing, print, calculators, and search engines. History shows that learners adapt. New tools shift what we learn and how we learn it. This report examines how AI-based offloading affects learning, draws on recent research (including Kosmyna et al., 2025) on brain activity with AI writing tools, and argues for responsible AI integration in education. Our core claim is simple: use AI to augment human cognition, not replace it. We offer a brief history, review current evidence, and present recommendations for policy and practice.

The bulk of the emerging L&D model—the 90%—is about re-coupling work and learning through AI-powered performance support. In practice, this means embedding support and “productive friction” within the workflow itself rather than locating it classrooms or LMSs.

Exactly how this plays out is to TBD, but on the ground at the “bleeding edges” of L&D experimentation I already see a commitment to reducing investment in online courses and in person workshops, in favour of AI “copilots” integrated directly where work happens.

In Teams/Slack channels, docs and CRMs, AI is on hand to help employees to draft artefacts, consider alternative approaches, weigh-up decisions and retrieve information from the organization’s knowledge base using retrieval-augmented generation (RAG).

This is the underlying hum AI has created in academia: my students are using AI to cheat, and there's not much I can do about it. When I asked one professor, who asked to be anonymous, how he catches students using AI to cheat, he said, "I don't. I'm not a cop." Another replied that it's the students' choice if they want to learn in class or not.

Employees across every function are having the same realization: “I need to stay valuable and effective in my role as AI transforms how work gets done.” Organizations face their own version: “We need our workforce to confidently adopt and apply AI capabilities without disrupting productivity or requiring massive retraining investments.”

The jobs to be done go beyond skills acquisition. Employees need to feel confident navigating AI tools, be perceived as forward-thinking contributors, and make tangible progress in their daily work. Organizations need to focus on teaching their employees to use AI effectively and ethically as they build adaptive capability at scale, all while maintaining operational excellence.

Traditional learning solutions consistently fail to fulfill these requirements. Yet the solution is not found in simply hiring more tech talent, sending people to online classes, or even declaring that the future is agentic. Instead, the answer lies in a systematic reinvention, where organizations build a learning system oriented around jobs to be done. It requires creating an operating system for permanent reinvention and adaptability that increases capability, enables agency, and scales leadership.

A learning system enables people to continually learn in the flow of work, moving beyond single courses or formal training into holistic, guided learning journeys that drive faster time to competency. By combining diverse modules, modalities, and formats, such as snackable content, hands-on practice, assessments, and certifications, Udemy creates a flywheel of skills development: sparking curiosity, deepening mastery, and proving skills while delivering measurable performance improvement

Nos encontramos en el umbral de una transformación que trasciende cualquier revolución pedagógica anterior. La IA no representa simplemente otra herramienta tecnológica que debemos integrar en nuestras aulas; constituye el catalizador de una metamorfosis cognitiva que está redefiniendo la naturaleza misma del pensamiento, el aprendizaje y la construcción del conocimiento humano.

Esta metamorfosis plantea una interrogante fundamental que atraviesa los cimientos de nuestro sistema educativo: ¿cómo debe evolucionar la educación cuando la cognición humana se fusiona inexorablemente con la IA para crear formas híbridas de pensamiento que superan las capacidades de ambas por separado?

Según Tarrant, la IA presenta oportunidades concretas para simplificar tareas rutinarias, lo que facilita que los educadores enfoquen su trabajo en aspectos significativos, como el análisis profundo o la creatividad. Esta perspectiva redefine el rol del profesor en la escuela y en la universidad, donde la transmisión mecánica de conocimientos cede ante la promoción de la reflexión, el debate y la generación de ideas propias por parte de los estudiantes .

Gardner, speaking during a forum Wednesday at the Harvard Graduate School of Education, called AI as fundamental a change to education as the world had seen in 1,000 years — and even said it may render obsolete many of the forms of mind he is famous for describing. 

“The need to have everybody in the class doing the same thing, being assessed in the same way, will seem totally old-fashioned,” he said. 

Advances in large language model (LLM) technology enable chatbots to generate and analyze content for our work. Generative chatbots do this work by predicting responses rather than knowing the meaning of their responses. In other words, chatbots can produce coherent-sounding but inaccurate or fabricated content, referred to as hallucinations. When humans uncritically use this untruthful content, it becomes what we call botshit. This article focuses on how to use chatbots for content generation work while mitigating the epistemic (i.e., the process of producing knowledge) risks associated with botshit. Drawing on risk management research, we introduce a typology framework that orients how chatbots can be used based on two dimensions: response veracity verifiability and response veracity importance. The framework identifies four modes of chatbot work (authenticated, autonomous, automated, and augmented) with a botshit-related risk (ignorance, miscalibration, routinization, and black boxing). We describe and illustrate each mode and offer advice to help chatbot users guard against the botshit risks that come with each mode.

AI Chatbots — including ChatGPT and Gemini — often cheer users on, give them overly flattering feedback and adjust responses to echo their views, sometimes at the expense of accuracy. Researchers analyzing AI behaviours say that this propensity for people-pleasing, known as sycophancy, is affecting how they use AI in scientific research, in tasks from brainstorming ideas and generating hypotheses to reasoning and analyses.

“Sycophancy essentially means that the model trusts the user to say correct things”

En Austin, Texas, un colegio comprime en dos horas lo que cualquier otro hace en ocho. Todas las asignaturas, de matemáticas a lengua, caben ahí. El resto del tiempo, los alumnos lo dedican a otras actividades: hablar en público, trabajar en equipo o sencillamente realizar un proyecto personal. Se deja espacio para el desarrollo de habilidades blandas.

Durante años nos han repetido que estamos ante la “generación mejor preparada”, pero los datos cuentan otra historia. Este video desvela cómo detrás del aumento de escolarización se esconde una caída alarmante en la calidad educativa. Jóvenes con másteres pero sin competencias reales, universidades convertidas en fábricas de diplomas y un sistema incapaz de preparar para el futuro digital. Analizamos el mito del progreso educativo, los factores que lo alimentan y cómo afecta a la sociedad, la economía y la libertad individual. Si crees que estudiar más equivale a saber más... este video te hará dudar.

Teachers are tough to crack. Thomas Edison believed film would replace teachers1, BF Skinner believed they would be replaced by ‘teaching machines’ and Sal Khan (of Khan Academy) argued in ~2015 that information transfer would soon primarily happen through video.2 Perhaps the reason these predictions were wrong is that we still don’t fully understand what great teaching is. It’s instruction and mentorship and coaching and pastoral support combined in a complex combination. There is a reason why Jean-Jacques Rousseau believed he would only be able to teach one other person in his lifetime, and at a stretch. Now some think AI puts teaching jobs at risk but I don’t think this is right for three reasons.

Los autores de "Which Humans?", de la Universidad de Harvard, cuestionan el tan mencionado "rendimiento humano" ejecutado en los Modelos de Escala Grande (LLM) de Inteligencia Artificial. Los autores argumentan que los humanos son una especie multicultural que presenta una diversidad psicológica sustancial en todo el mundo, una diversidad que los datos textuales utilizados para entrenar los LLMs no logran capturar completamente. Los hallazgos de su investigación demostraron que las respuestas de los LLM a las mediciones psicológicas son valores atípicos en comparación con los datos transculturales a gran escala.

Prepare students for a future where AI Tutors, emotion-aware classrooms, and immersive VR lessons are the norm — and discover what’s quietly reshaping education behind the scenes.

AI has already had a major impact on how students approach their learning, yet the technology is only just getting started. Wondering what else is on the horizon?

We’ll cover a few emerging trends and what educators can expect from AI lessons of the future. Hint — classes may not be limited to the classroom forever...

When ChatGPT first appeared in November 2022, many educators and academic institutions responded with blanket bans, unsure whether these tools were more likely to spark innovation or cheating. The jury is still out on that, but administrators have started to relax some policies and give teachers greater freedom to direct when they can be used. How these tools are used, however, is often driven by students.

Cada año, Chegg tiene como objetivo descubrir las perspectivas de los estudiantes sobre los problemas clave más apremiantes que dan forma a sus vidas, desde las finanzas y la salud mental hasta la creciente influencia de la tecnología y sus esperanzas para el futuro. En la Encuesta Global de Estudiantes 2025 les formularon una amplia gama de preguntas sobre el uso de la IA Generativa, cómo creen que cambiaría el panorama educativo y cómo deberían reaccionar las instituciones en respuesta. 

El análisis detallado de los hallazgos del estudio sobre la IA Gen en la Educación se centrará en la adopción, los beneficios, la visión de los estudiantes sobre el panorama educativo y las expectativas hacia las instituciones.

En Oppenheimer Presenta hablamos con expertos en educación e inteligencia artificial sobre las nuevas escuelas sin maestros y basadas en IA que empezaron a ponerse de moda en Estados Unidos. ¿Son efectivas? ¿Están al alcance de todos? ¿Qué cambios produce en la formación de los niños?

A top Google scientist and 2024 Nobel laureate said Friday that the most important skill for the next generation will be “learning how to learn” to keep pace with change as Artificial Intelligence transforms education and the workplace.