In recent years, the nexus between education and artificial in-
telligence has evolved rapidly. AI innovations, especially generative
AI, are already transforming teaching and learning. As machines become “smarter” and labor markets and economies get transformed, education systems must rethink their operating models to ensure that they can skill, upskill, and reskill individuals for the jobs of the future. HEIs are at the forefront of this change, given their “feeder” role for labor markets.

While there is no single definition for the term “Artificial Intelligence” (AI), it generally represents data-intensive systems that can perform tasks that are typically associated with human intelligence. While definitions vary, three key attributes typically characterize AI systems: (i) They are machine-based systems; (ii) They can infer outputs (predictions, content, or decisions) based on human objectives; (iii) These outputs are often indistinguishable from those of humans. The relationship between AI and higher education systems is neither linear nor unidirectional.

On the one hand, AI systems are significantly shaping teaching and
learning experiences in HE. On the other hand, HEIs could play a role in training AI-ready workforce, equipping learners for an AI dominated world, and shaping research and the policy discourse on the role of AI in society (see Illustration 1). The field of AI emerged on a university campus, when an American university professor organized the Dartmouth Summer Research Project on Artificial Intelligence in 1956.

As the technology becomes more pervasive, universities and TVET
institutions will play a key role in skilling and reskilling individuals for AI-dominated or -influenced labor markets. Additionally, HE researchers will shape both the technical dimensions (development, testing, design, efficacy, and accuracy) and policy discourse around artificial intelligence systems.

Los contextos de la educación superior en América Latina y el Caribe (ALC) han sido testigos de una creciente utilización de tecnologías y datos digitales, lo que ha dado lugar a nuevas actividades y alteraciones de las funciones existentes. Sin embargo, los desafíos en la región siguen siendo inmensos, incluyendo el acceso variable a Internet, habilidades digitales y capacitación limitadas, falta de infraestructura tecnológica y recursos deficitarios. La UNESCO ha estado trabajando para establecer el panorama general de la transformación digital en la educación, abogando por un enfoque de la tecnología y la conectividad basado en los derechos. En la educación superior, la transformación digital describe el proceso de mejora y cambio estratégicos mediante el aprovechamiento del potencial de las innovaciones tecnológicas en la enseñanza y el aprendizaje, el apoyo a los estudiantes, la investigación, la administración y el compromiso con la sociedad.

Esta publicación proporciona información valiosa sobre cómo la transformación digital en la educación superior (TDES) se puede implementar en ALC e identifica los desafíos actuales. Se basa en una revisión bibliográfica multilingüe y en una consulta a múltiples partes interesadas. En la consulta participaron nueve funcionarios gubernamentales y representantes de asociaciones nacionales de universidades públicas y privadas, así como 1,420 estudiantes de 12 universidades de Argentina, Brasil, Chile y Venezuela.

Más allá de afectar a millones de estudiantes y al personal educativo, el legado emergente de la pandemia es una profunda remodelación de la agenda de digitalización de la educación superior en ALC. Ha influido significativamente en las percepciones de las partes interesadas con respecto a la integración de las tecnologías de la información y las comunicaciones (TIC) en la educación superior (ES). Aunque ha habido un gran impulso para volver a las actividades presenciales, un número significativo de estudiantes ha seguido estudiando en modalidades híbridas.

En el pasado artículo se presentaron las principales tendencias tecnológicas estratégicas e Gartner para 2025. En el mismo se mencionó el próximo nivel de desarrollo de la Inteligencia Artificial: La Agentic AI. En este artículo, definiremos el concepto de la IA Agentiva, sus principales carcterísticas, beneficios, limitaciones y su impacto en el desarrollo cognitivo de los trabajadores. Comencemos el proyecto investigativo...

Los términos Memoria Expandida de (Andy Clark y David Chalmers) y Memoria Extendida, de Sergio Bosio se utilizan de manera intercambiable para describir el concepto de que nuestra capacidad para recordar y procesar información se amplía más allá de los límites de nuestro cerebro, gracias a herramientas externas, tecnologías y entornos.

Los autores mencionados ven una relación de coevolución entre la mente humana y la tecnología. Al utilizar herramientas, modificamos nuestras capacidades cognitivas y, a su vez, desarrollamos nuevas tecnologías para satisfacer nuestras necesidades cambiantes. Tanto Clark, Chalmers como Bosio, desde diferentes perspectivas, conciben la mente como un sistema distribuido que se extiende más allá del cerebro biológico, incluyendo el cuerpo, las herramientas y el entorno social.

La educación superior está viviendo una transformación profunda que está reconfigurando no solo la manera en que enseñamos y aprendemos, sino también la gobernanza y la estructura misma de las universidades. En un contexto global donde la tecnología avanza a un ritmo vertiginoso, los algoritmos y la Inteligencia Artificial (IA) se han convertido en herramientas clave para implementar modelos disruptivos que redefinen la pedagogía, la gestión académica y la relación entre docentes y estudiantes. Este cambio, impulsado por la Educación Disruptiva, se aleja de los modelos rígidos y centralizados para abrazar un enfoque más flexible, inclusivo y adaptado a las necesidades individuales de los estudiantes.

El concepto de “ubicuo” con sus diferentes perspectivas en términos simples, significa que algo está presente en todas partes a la vez, y si como si tuviera la capacidad de estar en múltiples lugares simultáneamente.

Teacher shortages have intensified across several OECD countries, making this an urgent priority for education systems. Between 2015 and 2022, the share of students whose principals reported shortages rose from 29% to 46.7% on average across the OECD. Simultaneously, rapid technological advances, such as Artificial Intelligence (AI), and broader socio-economic shifts, increase the need to support teachers in delivering quality education in evolving contexts. Addressing both the number of teachers and ensuring they possess the skills to meet new educational needs is essential for student success.

This report presents a policy roadmap to help education systems balance the supply and demand for quality teaching in these changing times. Drawing on responses from 33 education systems to the Education Policy Outlook National Survey for Comparative Analysis, as well as additional evidence, the report outlines current challenges and key factors in their interplay. It also explores policy responses to attract, retain, and develop teachers, enhancing their teaching practices and professional learning. In doing so, the report aims to support countries in advancing the goals set by the 2022 OECD Declaration on Building Equitable Societies Through Education. This report is part of the Education Policy Outlook series – the OECD’s analytical observatory of education policy.

In our deep dive into the AI agents space, we’ll outline Menlo’s thesis for the emerging market—starting by defining what agents are and what made them possible. We’ll trace the architectural evolution of the modern AI stack from few-shot prompting through retrieval-augmented generation (RAG) to full-fledged agentic systems, before exploring the implications of this paradigm shift for the application and infrastructure layers in later articles.

Esta investigación presenta un modelo de análisis diseñado para detectar, organizar y clasificar la singularidad en la educación superior basado en estudios de futurización. Para ello, se recopilan evidencias con el fin de identificar rasgos diferenciales en el campo educativo, lo que permite a las universidades tomar decisiones estratégicas en entornos complejos. El estudio se fundamenta en técnicas de análisis respaldadas por investigaciones futurísticas para identificar tendencias y organizaciones innovadoras en diversos contextos educativos.

Estas singularidades se clasifican y organizan mediante una propuesta metodológica mixta que combina investigación confirmatoria y la recopilación de datos cualitativos y cuantitativos. Los resultados del estudio proporcionan una visión de 55 organizaciones de educación superior con características singulares, destacando aspectos importantes de cada una de ellas. Además, se valida la utilidad del instrumento desarrollado como una herramienta crucial que permite a las universidades revisar y adaptar constantemente sus prácticas educativas, manteniéndolas actualizadas y respondiendo a los avances sociales y tecnológicos en un mundo en constante cambio.

Asimismo, la investigación busca fomentar un debate sobre el papel futuro de las universidades como actores en una sociedad dinámica, compleja e incierta. Esto resalta la importancia de que las universidades estén preparadas para enfrentar los desafíos y aprovechar las oportunidades que surgen en este contexto cambiante.

Khanmigo, an AI-powered online tutor, could change the way teachers work and students learn. Created by Khan Academy, the new technology is being piloted in 266 school districts.

Leadership coaching is the process of nurturing new and experienced leaders with personalized, one-on-one support from expert, certified coaches. These coaches provide guidance to help leaders navigate specific business challenges, develop skills and competencies, and reach their professional goals. Coaches can also serve as trusted advisors who provide ongoing feedback about real-world projects and mentor leaders by sharing lessons from their own experiences.

Discussions about education usually assume implicit trust in an authority, be it a teacher or an institution and its services (such as a platform or AI). Although conversational AI interactions do not allow us to interact with the AI creators, the scope and limitations of their designs communicate what they seek us to achieve and not achieve. For the most part, we may find a consensus based on human rights, whose foundations can be shared by multiple countries, but when considering individual people, what are our expectations of AI to help us grow?

Comp Lit 2BW will be the first course in the UCLA College Division of Humanities to be built around the Kudu artificial intelligence platform. The textbook: AI-generated. Class assignments: AI-generated. Teaching assistants’ resources: AI-generated.

While that might make it sound like Stahuljak is ceding her wide-ranging expertise to bots, she said the opposite is true. The new technology, she said, will have immediate tangible benefits for her, teaching assistants and — critically — students.

Khan’s latest project, dubbed Khanmigo and launched in 2023, provokes some of the same fears. Pairing generative AI with a user-friendly interface, the application, which is being piloted by over 600,000 students and teachers in the U.S., promises to deliver a personalized tutor to every classroom, allowing students to plug in and receive instruction on subjects ranging from elementary math to essay writing. Instead of simply providing answers to their questions, Khan says, new AI bots like Khanmigo are trained to serve as “thoughtful” mentors, prodding students with questions, giving them encouragement, and delivering feedback on their mistakes as they work to develop their own understanding.

Could Optimus change how classrooms operate? As a teaching assistant, it could handle tasks like preparing materials and supervising students during activities. This could reduce the administrative burden on teachers, allowing them to engage more with students. In special needs education, Optimus could provide personalized instruction and physical assistance, improving the learning experience for students requiring extra support.

In today’s educational landscape, generative AI is emerging as a powerful tool that can change the way we teach and learn. For educational leaders, Generative AI's potential is both vast and inspiring, offering significant opportunities to enhance educational outcomes, foster creativity, and, most importantly, prepare students for a future where AI is an integral part of every work and life.

Generative AI tools are versatile and capable of supporting a wide range of educational modalities. From personalized learning experiences to collaborative projects, generative AI will be able
to adapt to each student's unique needs, offering tailored support to help maximize individual potential. Imagine classrooms where an educator’s lesson plans are dynamically customized to suit the pace and style of each learner, or where virtual tutors provide instant feedback and encouragement. These AI-driven experiences are starting to become a reality in some educational environments.

We still prioritize efficient knowledge delivery, a method of learning in which the teacher doles out answers to their student. With modern technology, however, anyone can have their question answered through a quick Google Search. In today’s classrooms, students no longer need knowledge acquisition but creativity and problem-solving skills training. These essential skills set us apart as humans and receive surprisingly little emphasis.

Every job in the economy can be thought of as a bundle of tasks, shared between humans and machines. Over the years, software has picked off more and more of these tasks, but even today, humans still own the vast majority of business processes. In every function, headcount costs dwarf software expenditures by orders of magnitude.

AI agents promise to shift this balance of work decisively. Unlike previous generations of software that primarily addressed low-level, sequential tasks that could be robotically executed, new cognitive architectures enable agents to dynamically automate end-to-end processes. This is not just AI that can read and write—but ones that can decide the flow of your application logic and take actions on your behalf.

According to research from McKinsey & Company, only half of business leaders said their organizations are well-prepared to react to future shocks and disruptions. Meanwhile, a recent survey from AlixPartners found that 59% of CEOs are worried about losing their job due to disruption.

New and emerging leaders aren’t the only ones who need support during disruption. Even experienced executives may require support to willingly embrace change and lead others through it. Every leader could benefit from having an experienced confidant and advisor in their corner to help make difficult decisions. 

The AI Classroom is already with us. It’s not about robot teachers, or VR headsets: instead, the AI classroom offers technologies that ‘upgrade’ flesh-and-blood teachers and make new educational experiences possible. The AI education market is forecast to grow at an incredible rate this decade, hitting $88.2 billion worldwide by 2030, all driven by technologies that create lesson plans, mark papers and track progress. But perhaps the most exciting application of AI technology in education is in upgrading the way lessons are taught, and the way children learn.

En este contexto, los sistemas de aprendizaje personalizado ofrecen un enfoque innovador y eficiente que permite una educación adaptativa basada en la detección de patrones de aprendizaje individuales. Según Andrew Ng, cofundador de Coursera y líder en el campo de la IA, “el aprendizaje personalizado es la clave para que la tecnología permita a cada estudiante aprender a su propio ritmo” (Ng, 2018). Este cambio en los métodos de enseñanza responde a la demanda de modelos que integren enfoques inclusivos y colaborativos, donde cada estudiante pueda interactuar y aprender de forma única. La integración de Machine Learning en estos procesos permite, además, optimizar los recursos y adaptar los contenidos a las habilidades específicas de cada estudiante, un enfoque vital en una era de habilidades demandadas y conocimiento interconectado.

El enfoque disruptivo que estamos proponiendo reconoce que el aprendizaje es un proceso dinámico, donde el estudiante ocupa un rol central, colaborando con sistemas de IA para co-crear conocimiento. Juan Domingo Farnós, reconocido por sus aportaciones a la educación disruptiva, sostiene que «la convergencia entre la educación y la inteligencia artificial permite una evolución en los métodos pedagógicos, donde los alumnos no son meros receptores de información, sino agentes activistas en su proceso de aprendizaje». Este paradigma emergente se apoya en tecnologías como redes neuronales profundas, procesamiento de lenguaje natural y sistemas de análisis emocional, que habilitan a las instituciones a ofrecer experiencias educativas más significativas y contextualizadas.