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).

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. 

La Educación Superior latinoamericana no siempre prepara a los jóvenes para el futuro laboral. Mientras la inestabilidad económica e Inteligencia Artificial transforman el mercado de trabajo, millones de jóvenes quedan atrapados entre la la informalidad y la desigualdad.

Este episodio explora por qué la universidad, tal como la conocemos, podría no ser suficiente y qué cambios urgentes necesita para no quedarse atrás.

A recent study by Edweek indicates a low AI adoption rate among educators, highlighting a need for training and support, a trend likely mirrored by Instructional Designers. Despite AI's rapid integration into ID, a gap in AI expertise persists. Instructional Designers often explore AI's potential opportunistically. Thus, there is a need to develop a comprehensive AI skill set to leverage its full benefits. Although, by now, many materials exist to support Instructional Designers regarding AI integration in their craft, a holistic framework describing the skills required for Instructional Designers to master AI is still elusive.

This report introduces LearnWorlds' Framework, designed to help Instructional Designers master AI. Based on extensive research and supported by three AI-focused eBooks and developed AI tools, it positions LearnWorlds as a field leader, demonstrating unparalleled expertise and innovation. Assess your AI expertise across our Framework and set your personalized learning goals to master the AI expertise.

No basta con conocer el modelo de inteligencia artificial que vamos a utilizar; el verdadero poder está en la forma en que redactamos nuestras instrucciones, lo que hoy conocemos como prompts. La calidad de la imagen generada depende directamente de nuestro dominio del lenguaje que estos modelos entienden mejor. Dicho de otra manera, la creatividad ya no se mide solo en ideas, sino en la precisión con la que logramos describirlas para que una máquina las traduzca en imágenes.

Far too little is understood about the advances of Artificial Intelligence (AI) and its implications throughout society. In the education sector, there is much discussion on what AI can do to change the delivery of education, but little is known about how AI is shifting the ground rules as to what students should learn to complement AI capabilities effectively. If public policy wishes to do more than adapting curricula and instructional system post-hoc to every new AI tool that is thrown into the market, it needs to take an active role to anticipate the evolution of AI capabilities.

This report outlines a methodology to do exactly that. This methodology provides a set of indicators along key dimensions of human capabilities that each describes the development of AI towards full human equivalence. These indicators describe: Language; Social interaction; Problem solving; Creativity; Metacognition and critical thinking; Knowledge, learning and memory; Vision; Manipulation; and Robotic intelligence. The indicators are presented in scales of five levels, where the most challenging capabilities for AI systems are found towards the top. Grounded in human psychology, this approach offers a structured and high-level perspective on AI development.

High School students, especially 12th graders, are reading and learning math and science at historic lows, according to a new report from the National Assessment of Education Progress.

The new report, known as the Nation's Report Card, was released Monday by the National Center for Education Statistics, or NCES, and the Department of Education. It is the first nation's report card to be released since the coronavirus pandemic.

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.

The Middle States Commission on Higher Education (MSCHE) continues to encourage innovation in higher education. The Commission has approved new Use of Artificial Intelligence Policy and Procedures that reflect this commitment and incorporate emerging research and best practices in this areas:

1. Use of Artificial Intelligence Policy
2. Use of Artificial Intelligence Procedures

The OpenAI's research shows that about 10.2% of all messages are tutoring/teaching, and within the “Practical Guidance” category, tutoring is 36%. “Asking” interactions are growing faster than “Doing” and are rated higher quality by users. Younger people contribute a huge share of messages, and growth is fastest in low- and middle-income countries (How People Use ChatGPT, 2025).

If AI is already acting as a global tutor, the question isn’t “will people learn with AI?”—they already are. The real question we need to ask is: what does great learning actually look like, and how should AI evolve to support it? That’s where decades of learning science help us separate “feels like learning” from “actually gaining new knowledge and skills”.

Despite the rapid adoption of LLM chatbots, little is known about how they are used. We document the growth of ChatGPT’s consumer product from its launch in November 2022 through
July 2025, when it had been adopted by around 10% of the world’s adult population. Early adopters were disproportionately male but the gender gap has narrowed dramatically, and we find higher growth rates in lower-income countries. Using a privacy-preserving automated pipeline, we classify usage patterns within a representative sample of ChatGPT conversations. We find steady growth in work-related messages but even faster growth in non work-related messages, which have grown from 53% to more than 70% of all usage. Work usage is more common for educated users in highly-paid professional occupations.

We classify messages by conversation topic and find that “Practical Guidance,” “Seeking Information,” and “Writing” are the three most common topics and collectively account for nearly 80% of all conversations. Writing dominates work-related tasks, highlighting chatbots’ unique ability to generate digital outputs compared to traditional search engines. Computer programming and self-expression both represent relatively small shares of use. Overall, we find that ChatGPT provides economic value through decision support, which is especially important in knowledge-intensive jobs.

David Cutler afirma que la GenAI no es una solución transformadora para la Educación. Puede desempeñar un papel de apoyo en el aula, pero nunca puede reemplazar lo que realmente funciona: maestros capacitados y dedicados que trabajan directamente con los estudiantes mientras luchan con la dura y necesaria lucha del aprendizaje que lleva tiempo dominar.

Citando varios estudios, Cutler establece que la eficiencia generativa de la IA, no significa que haya ganancia cognitiva en el proceso de aprendizaje. El uso excesivo de la IA debilita las conexiones mentales que son importantes para activar el pensamiento crítico y analítico, procesos clave del pensamiento cognitivo de orden superior y la agencia. 

Mi pensar es, que la Educación debería centrarse más en el desarrollo cognitivo del aprendiz, que en la eficiencia tecnológica para mejorar la enseñanza docente en el aula. 

La 10ma edición del informe Changing Landscape of Online Education (CHLOE), revela un aumento significativo en la demanda de aprendizaje en línea por parte de los estudiantes en medio de un entorno cada vez más competitivo a medida que la IA se avecina sin una estrategia coordinada para su uso.

Los hallazgos del informe incluyen:

1. El interés en línea aumenta en las poblaciones estudiantiles: Desde estudiantes adultos hasta estudiantes universitarios tradicionales, los COL informan una demanda sostenida y creciente de educación en línea.
2. La preparación institucional se tambalea en medio de la creciente demanda:  Solo el 28% de los profesores se consideran completamente preparados para el diseño de cursos en línea y el 45% para la enseñanza. 
3. El mercado de la educación en línea es cada vez más competitivo: En todos los sectores, los COLO describen un panorama de programas en línea más competitivo que antes de la pandemia.
4. Las credenciales alternativas ocupan un lugar central: La inversión en ofertas sin título como certificados, microcredenciales y bootcamps ha aumentado.
5. La integración de la IA carece de coordinación estratégica: Dos tercios de los COOL informan que algunas áreas institucionales están trabajando en una estrategia de IA, pero pocas tienen un plan unificado o coordinado. El nueve por ciento no tiene ninguna estrategia.