En la mayoría de los entornos educativos actuales, el uso de la inteligencia artificial se limita a “googlear con IA”: buscar respuestas rápidas, copiar y pegar y obtener ideas superficiales. Esta práctica, aunque útil en ciertos contextos, desaprovecha el potencial formativo de estas herramientas.

Recientemente con estudiantes de la asignatura “Tecnología e innovación educativa” de Ciencias de la Educación, propuse que dejemos de googlear con la IA. El tema de la clase fue la creación de una infografía, desde su fundamento pedagógico y sus posibilidades didácticas hasta el diseño y el programa para su creación.

Key Findings:

• 61% of college students surveyed reported heavy reading to be a top factor in academic burnout. The average full-time college student surveyed spends 28 hours a month reading and comprehending course materials to complete assignments.
• 9/10 college students surveyed are using Generative AI for their classwork, with over half (54%) using it at least once a week, according to our survey.
• 80% of students reported being worried that the generative AI tools they use for their courses provide inaccurate or misleading information.
• 77% of college students surveyed say that colleges should be offering AI skills classes to better prepare them for careers.
• 79% of four-year universities whose policies we analyzed allow the use of AI for study preparation (i.e. notecard creation, study guide outlines).

Se estima que entre el 20 y 40 por ciento de las horas docentes actuales se dedican a actividades que podrían automatizarse utilizando la tecnología existente. En el ámbito educativo, la tecnología se vuelve una herramienta clave para ayudar a los educadores acelerar tareas repetitivas y centrarse en las tareas de enseñanza que más agregan valor a los estudiantes.

La evaluación del conocimiento es un aspecto fundamental en el marco educativo, ya que permite medir el progreso y el aprendizaje de los estudiantes. La construcción y aplicación de sistemas de evaluación proporciona información sobre las fortalezas y debilidades en el aprendizaje, lo que permite a los educadores adaptar su enseñanza para mejorar los resultados. Sin embargo, la construcción de estos artefactos de evaluación tienen una procedimiento definido que debe seguirse para garantizar la calidad de las evaluaciones. Por esta razón, la Taxonomía de Bloom es una herramienta útil para guiar la evaluación del conocimiento en el aula. Al utilizar la taxonomía de Bloom, los educadores pueden diseñar evaluaciones que midan diferentes niveles de comprensión y habilidades de pensamiento crítico.

The rapid proliferation of AI in our lives introduces new challenges around authorship, authenticity, and ethics in work and art. But it also offers a particularly human problem in narrative: How can we make sense of these machines, not just use them? And how do the words we choose and stories we tell about technology affect the role we allow it to take on (or even take over) in our creative lives? Both Vara’s book and The Uncanny Muse, a collection of essays on the history of art and automation by the music critic David Hajdu, explore how humans have historically and personally wrestled with the ways in which machines relate to our own bodies, brains, and creativity. At the same time, The Mind Electric, a new book by a neurologist, Pria Anand, reminds us that our own inner workings may not be so easy to replicate.

According to Frannie, “All of the universities are trying to figure out what to do with education with ChatGPT… The question is what is it that [students] are learning in the first place? Are they learning to be citizens who blindly use ChatGPT because that's what everyone will be doing in the future? Will that be enough? Or do they have to learn more of the basics before delving into these assistance tools? That’s what we're trying to learn in the first place, ‘what's the skill?’”

Agency without self-efficacy resembles aimlessness. A learner with options but lacking skills or confidence may disengage. Self-efficacy without agency becomes obedience. Learners follow others’ plans but don’t lead their own. Empowerment emerges when people are free and able to make meaningful decisions, reflect on progress, and adapt with purpose.

Too often, schools fall short on both. What is presented as “alternatives” or “choices” becomes a set of false selections, based on shallow decisions such as electives, paint colors, or seating arrangements, while competence is measured through high-stakes tests. Learners navigate a system that respects neither autonomy nor confidence. To change this, learning environments must support both freedom and growth.

Artificial Intelligence has become the new geopolitical fault line – and universities now sit squarely on it. Washington’s export-control regime blocks sales and technical support for advanced AI chips to China; Beijing, for its part, requires recommendation algorithms and generative-AI models to be filed with – and in some cases to be licensed by – state regulators; and Brussels has approved the world’s first cross-sector ‘trustworthy AI’ act.

In this paper, the authors present a complete analysis carried out for reporting the lack, needs and requested competences and skills on the Key Enabling Technologies (KETs) of the Industry 4.0 (I4.0) in both Higher Education (HE) as well as in the wood, furniture and manufacturing sector all over Europe. Moreover, this analysis evaluates the level of implementation in this specific sector considering latest technological trends. The analysis was performed within the framework of the European project MAKING 4.0, where a complete set of surveys were launched to agents who were directly involved in the wood, furniture and manufacturing sector to find out what their impressions are about the current knowledge and the one demanded by the I4.0, obtaining results where the low level of competences and qualifications in this industrial sector is demonstrated, both in education and in industry, as well as the poor training offered in I4.0 and its KETs. It is concluded that there is an evident technological skills gap between those acquired by workers and by Vocational Education Training (VET) and HE students regards the demands of the I4.0, which must be covered within the next years, through training initiatives, enabling and acquisition of needed competences such as, for example, MAKING 4.0.

Artificial Intelligence (AI) is rapidly transforming the modern world, driving innovation across industries, enhancing productivity, and reshaping the way we live and work.  To ensure the United States remains a global leader in this technological revolution, we must provide our Nation’s youth with opportunities to cultivate the skills and understanding necessary to use and create the next generation of AI technology.  By fostering AI competency, we will equip our students with the foundational knowledge and skills necessary to adapt to and thrive in an increasingly digital society.  Early learning and exposure to AI concepts not only demystifies this powerful technology but also sparks curiosity and creativity, preparing students to become active and responsible participants in the workforce of the future and nurturing the next generation of American AI innovators to propel our Nation to new heights of scientific and economic achievement.

Over the past few weeks, I've been deep in conversation with L&D leaders across multiple Fortune 500 companies about the future of our profession. At the same time, a number of important articles have been published showing what the likely future of learning and development in the workplace will look like.

Together, these developments have revealed something important: despite widespread anxiety, the instructional design role isn't dying—it's specializing.

What we're witnessing isn't the automation of instructional design and the death of the instructional designer, but rather the evolution of the ID role into multiple distinct professional pathways.

Tener docentes punteros en la investigación en su campo es uno de los objetivos fundamentales de cualquier universidad. Pero también interesa que tengan habilidades como enseñantes y sean capaces de motivar e inspirar a sus estudiantes.

Un equilibrio ideal entre ambas facetas se logra cuando los docentes universitarios, además de estar altamente cualificados en su campo de estudio, han sido formados en el análisis teórico y crítico de los procesos educativos, en el diseño de metodologías docentes y en la aplicación de estrategias de evaluación acordes con las exigencias del aprendizaje universitario.

AI has recently dominated discussions on the future of just about every sector, and its impact on education is no exception. Yet one in five teachers doesn’t think AI is appropriate for a K-12 setting.

This article examines the main ethical considerations when teaching AI to high school students (along with some suggestions for how to navigate them).

The advances of AI pose big questions for Education. Not only might AI Tutors, grading systems, or learning platforms become more sophisticated (and computationally more cost-effective); the entire nature of interaction between humans and machines could fundamentally change. Students may engage with Quantum-Enhanced AI agents that adapt to their learning style in real time, simulate future learning trajectories, or even collaborate with them in creative tasks. But as the computational logic behind AI becomes less transparent and more probabilistic, ensuring that these systems remain accountable, intelligible, and aligned with educational values will become a critical policy and design challenge.

Este informe examina el potencial transformador de la inteligencia artificial (IA) en la Educación Superior, centrándose en sus aplicaciones clave, los desafíos que presenta y las recomendaciones estratégicas para una integración ética. Las herramientas de IA ya están generando un impacto significativo en el apoyo a los estudiantes, la investigación académica y la gestión institucional. Estudios recientes sobre sistemas de IA Generativa muestran resultados prometedores; por ejemplo, las plataformas de asignación basadas en IA han aumentado la eficiencia en la colocación de estudiantes en un 20% y mejorado las opciones
de los estudiantes mal emparejados en un 38% (Larroucau et al., 2024).

Asimismo, investigaciones sobre herramientas de IA Generativa implementadas con rigor han evidenciado mejoras significativas en los resultados de aprendizaje: un estudio de Harvard encontró que los estudiantes que utilizaron Tutores IA aprendieron más del doble en menos tiempo en comparación con metodologías de aprendizaje activo (Kestin et al., 2024), mientras que un estudio de Stanford demostró que la tutoría mejorada con IA puede escalar las prácticas de enseñanza expertas, ayudando a los tutores a emplear estrategias pedagógicas más efectivas, con un costo accesible de 20 dólares anuales por tutor (Wang et al., 2025). Estas tecnologías están contribuyendo a reducir brechas de aprendizaje y a ampliar el acceso a una educación de calidad, abordando el “Problema de las 2 Sigmas” de Bloom (Bloom, 1984) mediante apoyo personalizado y 24/7, que complementa los métodos tradicionales de enseñanza.

Sin embargo, para aprovechar todo el potencial de la IA en la región de LAC es necesario superar obstáculos críticos. Estos incluyen la brecha digital, que limita la accesibilidad a la IA en las zonas rurales; la baja participación de las instituciones latinoamericanas en la innovación global de IA (contribuyendo sólo con el 0,21% de las patentes de IA); y las preocupaciones éticas en torno al sesgo algorítmico y la privacidad de los datos. Para hacer frente a estos retos es necesario un enfoque holístico que incluya inversiones en investigación sobre IA, programas de formación docente, mejora de la infraestructura digital y una mayor colaboración multisectorial.

El panorama de la adopción y despliegue de la IA revela tanto oportunidades como obstáculos significativos entre los docentes. Las encuestas realizadas entre docentes indican que, aunque el 61% ha utilizado la IA para enseñar y el 65% la considera una oportunidad, persisten importantes preocupaciones. En concreto, al 83% le preocupa la capacidad de los estudiantes para evaluar críticamente los resultados de la IA, y el 80% afirma que sus instituciones carecen de directrices exhaustivas sobre IA. Estos resultados subrayan la necesidad de sistemas sólidos de apoyo a docentes y políticas institucionales claras. Además, los primeros
indicios sugieren que, sin un diseño y una aplicación cuidadosos, las herramientas de IA pueden amplificar inadvertidamente las desigualdades educativas existentes, afectando en particular a los estudiantes de comunidades desfavorecidas y a los que no hablan su lengua materna.

The move towards distance learning has transformed access to education, removing geographical barriers and enabling both flexible and real-time teaching, no matter the location of the student or provider.

The integration of collaborative tools such as video calls, document sharing and virtual whiteboards promotes dynamic and interactive online learning experiences for students and educators alike. These technologies have proved particularly useful in recent years for ensuring continuity of teaching during periods of social distancing – when delivering in-person learning was impossible.

Our survey results clearly show a generational divide in workers' perception of college investment. While only 20% of Baby Boomers consider their degrees a waste of money, that number jumps to 41% for Millennials and a striking 51% for Gen Z.

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements—creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

Technology in today’s classrooms is advancing rapidly, reshaping the way students learn and teachers teach, especially with advancements in AI. Educators play a vital role in shaping meaningful and impactful learning opportunities for students through emerging technologies like AI. It’s more important than ever to provide teachers with tools and opportunities to explore technology to empower them as agents of change in the classroom.

One initiative aims to do just that: HP AI Spotlight Schools, a partnership between HP, Microsoft, Intel and Digital Promise. It features state-of-the-art computing devices, professional learning experiences and community resources. These schools are working to equip educators and students with essential skills to harness the power of AI in an increasingly modern technological world.

A college degree was once viewed as a surefire ticket to a good job and a clear pathway for upward mobility. However, concerns about the rising cost of college and the struggles of recent college graduates to find good jobs have led many Americans to lose confidence in higher education.

As a university educator for decades, I have witnessed profound transformations in how students learn, write and interact with knowledge. But nothing prepared me for a moment that occurred last year during a seminar I was leading on global ethics.

A student raised her hand and asked a question with quiet sincerity and startling clarity: “I asked ChatGPT to help me map out my argument. Then I used an AI tool to visualise the data and Grammarly to polish the writing with my guidance. I know it’s still my work – but does that count as mine?”

Like many teachers, I sometimes notice my unease growing as I read a piece of student work. Word by word my suspicion builds, and then all at once I understand. This is an artificial intelligence (AI)–generated text and not something my student actually wrote. Now what do I do?