La integración de la educación disruptiva y la IA-AGI en la educación superior, dentro del contexto de la realidad aumentada (RA) y la realidad extensiva (RX), abre un abanico de posibilidades que transforman cómo los estudiantes interactúan con el conocimiento. A través de un escenario multimodal, podemos explorar cómo estas tecnologías permiten personalizar, ampliar y hacer más inmersivo el proceso de aprendizaje.

Al integrar modelos de IA para personalizar el aprendizaje, hemos explorado cómo la memoria contextual y los agentes inteligentes pueden contribuir a un entorno educativo donde cada estudiante pueda avanzar a su propio ritmo, tomando decisiones informadas sobre su proceso de aprendizaje. Como afirman Goodfellow et al. (2016) en su obra Deep Learning, las redes neuronales profundas y los algoritmos avanzados pueden replicar la memoria cognitiva de los estudiantes, permitiendo a los sistemas aprender y adaptarse con base en sus interacciones previas, generando una experiencia educativa dinámica y enriquecedora. Este tipo de aprendizaje autónomo y personalizado se alinea con las aspiraciones de Farnós, quien sostiene que la IA puede servir para potenciar y revolucionar la educación superior mediante la creación de entornos inteligentes que se adapten a las necesidades de cada estudiante.

In a recent Boston Globe op-ed, two researchers proposed a linguistic fix to an ontological dilemma: rename our relationships with AI. Rather than referring to generative systems as “coworkers” or “collaborators,” they suggest we replace the human “co-” with a machine-coded “c0-”: c0worker, c0creator, c0mpanion. The goal is to reassert the boundary between human and machine by embedding it in our language—clarity by typography.

It’s a clever gesture, but telling. Faced with the entangled realities of cognitive labor shared across human and machine systems, the instinct is not to inquire but to quarantine. The prefix becomes a firewall. We’re no longer debating what AI is or does; we’re drawing thicker lines around who gets to count as meaning-making. This isn’t just terminological hygiene—it’s epistemological panic.

The persistence of the binary—human as subject, machine as tool—isn’t just about fear. It reflects an older philosophical architecture in which cognition is tied to consciousness, and meaning to interiority. When AI generates output that seems intelligent, we either mistake it for a person or strip it of all significance. The problem isn’t that machines confuse us. It’s that our frameworks for interpretation haven’t kept up.

The AI in Education Leadership Catalyst community of practice was intentionally designed to foster the strategic leadership and collaborative networks necessary to navigate the complex implications of generative AI. As co-designers, we envisioned a space where leaders could contemplate big questions: How do we lead with integrity when the ground beneath us may be shifting? How do we center pausing and introspection in an era of AI? And, most importantly: How can we design learning experiences that are as adaptive and resilient as the leaders we serve?

El universo tecnológico en el que estamos inmersos provoca cambios en el cerebro humano, que, plástico y moldeable como es, responde adaptándose a nuevos estímulos y experiencias. ¿Cómo lo hace? ¿Qué consecuencias tienen estos cambios a corto plazo? Y a largo, muy largo plazo, ¿marcará el cerebro digital un nuevo paso en la evolución humana?

The rise of artificial intelligence has schools racing to respond to tools like ChatGPT. While some fear it may replace teachers and erode human connection, others see it as a chance to transform and improve education.

As education shifts from traditional content delivery to students to teaching students how to develop and employ agile, critical thinking, and problem-solving skills, the teacher's role is being redefined. This transformation is essential to meet the United Nations' Sustainable Development Goal 4 (SDG 4), specifically targets 4.1 and 4.A, which aim to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.

Furthermore, the World Economic Forum in 2024, released a report that calls for an Education 4.0 Framework which can “serve as an inspiration for global leaders and practitioners.” The chapter explores “Who is the future schoolteacher?” and “How do we prepare them to teach?” It posits that the future educator will
be less a conveyor of information and more a facilitator of learning experiences that foster critical thinking, creativity, and adaptability.

This shift is driven by the imperative to prepare students for scenarios that do not yet exist, requiring a fundamental rethinking of teaching methodologies and the competencies that educators need to develop. This shift thus requires us to embrace change as
we prepare and engage future teachers in designing instruction that assists all learners in meeting that set of skills. Key recommendations from the 2024 World Economic Forum’s Education 4.0 Framework (World Economic Forum, 2024) will be considered as they relate to the preparation of future teachers.

Hearing the horror stories they have to share, it's not hard to see why Gehl and other educators feel that way. A Los Angeles-based teacher grimly estimated that 40% of the work that came across their desk "is touched by the hand of AI." Another who teaches postgrads was appalled that their students in a doctoral program about responsible AI gave in to lazily using the tech.

En este vídeo descubrirás el origen oscuro del sistema educativo y cómo fue diseñado por las élites para adoctrinar, manipular y fabricar obedientes trabajadores. Desde Prusia hasta Rockefeller, entenderás cómo convirtieron la educación en una herramienta de control masivo y en uno de los negocios más rentables de la historia. Esta no es una teoría: son hechos históricos que te abrirán los ojos. Si alguna vez sentiste que algo no cuadraba… esto lo explica todo.

When Sir Ken Robinson delivered his now-iconic TED Talk in 2006, proclaiming that “schools kill creativity,” he struck a chord that continues to reverberate through education systems worldwide. Robinson’s argument that our schools systematically squash imagination in favor of conformity and compliance sparked a movement for more creative, learner-centered approaches. Yet, less than two decades later, a new fear is echoing through these same halls: that artificial intelligence will now be the force that finally kills critical thinking in schools. How did we get from blaming the system to blaming the tool?

In much of the public debate, creativity and critical thinking are treated as either interchangeable or competing priorities, when in fact, both are essential to meaningful education and innovation. Yet, the same structures that kill creativity (e.g., expressed as imagination, originality, and divergent thinking) also discourage critical thinking (questioning, reasoning, and independent judgment). When students are rewarded for reproducing accepted answers rather than generating new ones or interrogating the old, neither skill is meaningfully developed.

Employer demand for formal degrees is declining for all jobs, but more quickly for jobs exposed to artificial intelligence, according to the 2025 AI Jobs Barometer report by professional services firm PwC published last week.

“AI helps people rapidly build and command expert knowledge ... which could make formal qualifications less relevant,” according to the report which analyzed close to a billion job ads and thousands of company financial reports across six continents.

This paper introduces "GAIDE: Generative AI for Instructional Development and Education," a novel framework for using Generative AI (GenAI) to enhance educational content creation. GAIDE stands out by offering a practical approach for educators to produce diverse, engaging, and academically rigorous materials. It integrates GenAI into curriculum design, easing the workload of instructors and elevating material quality.

With GAIDE, we present a distinct, adaptable model that harnesses technological progress in education, marking a step towards more efficient instructional development. Motivated by the demand for innovative educational content and the rise of GenAI use among students, this research tackles the challenge of adapting and integrating technology into teaching.

GAIDE aims to streamline content development, encourage the creation of dynamic materials, and demonstrate GenAI's utility in instructional design. The framework is grounded in constructivist learning theory and TPCK, emphasizing the importance of integrating technology in a manner that complements pedagogical goals and content knowledge.

Our approach aids educators in crafting effective GenAI prompts and guides them through interactions with GenAI tools, both of which are critical for generating high-quality, contextually appropriate content. Initial evaluations indicate GAIDE reduces time and effort in content creation, without compromising on the breadth or depth of the content.

Moreover, the use of GenAI has shown promise in deterring conventional cheating methods, suggesting a positive impact on academic integrity and student engagement.

If you’re an instructional designer, you’ve probably noticed how everything has changed over the past few years. What used to take us weeks to create can now be done in days thanks to artificial intelligence. Learners are no longer satisfied with boring PDFs—they want interactive experiences that engage them from the very first moment.

In 2025, instructional design is no longer just about “creating a course.” We now design learning experiences that adapt to each person, learn from their mistakes, and predict what they need before they even know it themselves.

And here’s the challenge: there are so many tools available that it’s easy to get lost. Which tools does an instructional designer really need? Whether you’re designing content for a university, a company, or a training platform, this guide will help you find the best instructional design tools for 2025.

This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools).

Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge.

Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity.

Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users.

Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.

The Artificial Intelligence revolution is transforming higher education at an unprecedented pace, offering innovative opportunities to personalize university learning experiences, support professors and researchers in their daily tasks, and optimize the management of educational institutions.

Higher Education in Latin America and the Caribbean requires urgent and creative actions to overcome the challenges affecting millions of university students in the region. Our goal is to provide the necessary tools to ensure that institutions and students make the most of the opportunities offered by AI. The report highlights areas such as: 

1. Personalized university learning 
2. Support for faculty and researchers 
3. Optimization of institutional management 
4. Advanced virtual tutors 
5. Innovation in assessment and feedback 
6. Scalable solutions for higher education institutions 
   
   

This report is part of the Digital Innovations in Education for Latin America and the Caribbean series. These academic studies and reports are designed to maximize the use of technology and encourage the effective application of digitalization in education. 

Vivimos un momento que es impresionante y desafiante a la vez: herramientas de IA que parecen sacadas de una película de ciencia ficción están al alcance de todos. Hoy cualquiera puede generar un correo, una presentación o un discurso en segundos... aún sin saber escribir profesionalmente.

Sin embargo… todo empieza a sonar igual. Las redes, los correos, las respuestas, los discursos.... todo suena igual. Los mensajes son técnicamente correctos, pero emocionalmente planos. Brillantes por fuera, vacíos por dentro.

Imagine having access to your target learners' thoughts, behaviours and emotional responses throughout your entire design process. While this may seem like science fiction (and, perhaps, risky), recent research by Park et al. (2024) demonstrates that AI personas built from intentionally gathered, structured data can predict learner behaviour more accurately than traditional human-centred approaches, achieving approximately 85% accuracy in behavioural simulation and prediction.

The use of Artificial Intelligence (AI) in education has the potential to further customise and personalise students’ learning, and encourage self-directed learning. It can also augment teachers’ professional practice by automating routine tasks and allowing teachers to spend more time with students and provide more complex guidance to advance learning. However, inappropriate AI use may impede the development of critical thinking skills in students or propagate misinformation.

In Singapore, the Infocomm Media Development Authority (IMDA) has introduced the Model AI Governance Framework to guide the responsible deployment of AI solutions. The Implementation and Self-Assessment Guide for Organisations (ISAGO) was also created to help organisations align their AI governance practices with the Model AI Governance Framework.  

Specifically, the Ministry of Education has developed the AI-in-Education (AIEd) Ethics Framework, which builds on the Model AI Governance Framework to address education-specific use and risks, and incorporates key beliefs from the Ethos of the Teaching Profession. 

¿Nunca te has preguntado por qué todos seguimos el mismo patrón? Es decir, cuando vamos a la escuela, aunque de niños todos pensamos de manera diferente, nos imparten la misma educación, la misma ideología, los mismos contenidos.

Nietzsche y Schopenhauer reflexionaron bastante sobre esto. De hecho, tenían ideas distintas, pero muy particulares sobre lo que representaba la educación y por qué cada estudiante debería ser educado de manera diferente.

En esta ocasión, haremos una exploración completa sobre la historia del sistema educativo: de dónde proviene, cómo llegó a Occidente y cómo se ha convertido en la mayor trampa del mundo moderno...

Picture this: a learner on a course you designed opens their laptop and types into ChatGPT: "I want to learn by teaching. Ask me questions about calculus so I can practice explaining the core concepts to you."

In essence, this learner has just become an instructional designer—identifying a gap in the learning experience and redesigning it using evidence-based pedagogical strategies.

This isn't cheating—it's actually something profound: a learner actively applying the protégé effect, one of the most powerful learning strategies in cognitive science, to redesign and augment an educational experience that, in theory, has been carefully crafted for them.

I honestly don’t know how I should be educating my kids. A.I. has raised a lot of questions for schools. Teachers have had to adapt to the most ingenious cheating technology ever devised. But for me, the deeper question is:

• What should schools be teaching at all? A.I. is going to make the future look very different.
• How do you prepare kids for a world you can’t predict?
• And if we can offload more and more tasks to generative A.I., what’s left for the human mind to do?

Rebecca Winthrop is the director of the Center for Universal Education at the Brookings Institution. She is also an author, with Jenny Anderson, of “The Disengaged Teen: Helping Kids Learn Better, Feel Better, and Live Better.” We discuss how A.I. is transforming what it means to work and be educated, and how our use of A.I. could revive — or undermine — American schools.

Over the past several years, educators have produced a seemingly endless string of task force reports, policy statements, op-eds, and other forms of hand-wringing about the role of AI in writing. But few have recognized what is abundantly obvious to pretty much everyone under the age of 25—that today’s young people will inhabit a future where the vast majority of writing will be produced using AI. 

Of course, there are exceptions. Novelists, reporters, screenwriters, columnists, attorneys, academics, and others in writing-intensive professions will still need to be skilled writers. For them, AI will complement and amplify—but not fully replace—human writing expertise.