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.  

Research by the student accommodation company Yugo reveals that: 

• 43%of UK university students are using AI to proofread academic work
• 33% use it to help with essay structure
• 31% use it to simplify information
• Only 2% of the 2,255 students said they used it to cheat on coursework

Universities need to become more flexible, adaptable and agile to ensure that students are prepared for a fast-moving, disruptive world impacted by evolving technologies such as artificial intelligence, sweeping trade disruptions, and significant demographic changes, a meeting of Pacific Rim university leaders heard.

Meeting on the margins of the Asia-Pacific Economic Cooperation (APEC) education ministers meeting in Jeju, South Korea, the APEC University Leaders Forum (AULF) focused on the theme: “Leveraging AI in higher education to address Asia Pacific challenges”.

La Inteligencia Artificial ha penetrado con determinación en cada rincón de nuestra sociedad, y la educación no ha sido una excepción. Sus primeros pasos en las aulas e instituciones educativas ya han redefinido la forma en la que enseñamos y aprendemos. Pero, como cualquier odisea de exploración, tras pisar un terreno, siempre nos sentimos impulsados a descubrir el siguiente. Más allá de las aplicaciones inmediatas y palpables que ya hemos explorado, existe un futuro que todavía no está escrito, lleno de posibilidades, desafíos y preguntas sin respuesta.

Esta guía es una invitación a la reflexión profunda, a la imaginación y a la definición conjunta de posibles futuros en los que la IA y la educación deberán convivir.

Aunque el futuro es una entidad esquiva e imprevisible, nuestra capacidad para imaginar nos proporciona un camino para explorar, al menos en parte, lo que podría esperarnos. No tenemos una bola de cristal que nos permita prever con precisión los acontecimientos que vendrán, pero sí podemos esbozar escenarios probables, e incluso deseables, sobre la intersección entre la IA y Educación.

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.

For many college students these days, life is a breeze. Assignments that once demanded days of diligent research can be accomplished in minutes. Polished essays are available, on demand, for any topic under the sun. No need to trudge through Dickens or Demosthenes; all the relevant material can be instantly summarized after a single chatbot prompt.

Welcome to academia in the age of Artificial Intelligence. As several recent reports have shown, outsourcing one’s homework to AI has become routine. Perversely, students who still put in the hard work often look worse by comparison with their peers who don’t. Professors find it nearly impossible to distinguish computer-generated copy from the real thing — and, even weirder, have started using AI themselves to evaluate their students’ work.

You need resources for creating eLearning, videos, presentations, websites, and animations, right? Here are eleven sets of freebies to help you get the job done. There was no effort to list AI tools (there are so many of them now!), but some are AI-enhanced.

Some tools and assets require registration to download. Please be careful when downloading freebies to be sure there is nothing malicious. I attempt to list reputable sites, but cannot vouch for each one. Enjoy!

One of the goals we've long talked about—but often struggled to implement—in instructional design is rapid prototyping. In other industries - from design thinking to product development and programming - building and testing early versions of ideas is a critical part of their work and proven to lead to better outcomes. Working prototypes spark clearer stakeholder feedback than static documents, and early user testing saves costly rework down the line.

Research shows that the same is true in instructional design. Rapid prototyping of courses, modules or even discrete activities is shown to improve instructional product quality, shorten development cycles, and enhance collaboration between designers and stakeholders (Daugherty et al., 2007; Gerber & Carroll, 2012). Compared to traditional linear “waterfall” models, prototyping enables instructional designers to integrate formative evaluation at every step, improving usability, learner engagement and likelihood of impact (Tripp & Bichelmeyer, 1990; Nixon & Lee, 2001).