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 push for A.I. in K-12 schools is now coming from the president, who issued an executive order on April 23 calling for the use of the technology in all grades. The secretary of education, Linda McMahon, previously said her goal is getting the federal government out of education, sending control back to the states and empowering parents, but apparently there’s a carve-out for nudging Big Tech’s continued incursion into the classroom.

The executive order claims that “A.I. education in kindergarten through 12th grade (K-12) is critical” because the United States needs “to develop an A.I.-ready work force and the next generation of American A.I. innovators.” The executive order commands the secretary of education to also “prioritize the use of A.I. in discretionary grant programs for teacher training” so that educators might “integrate the fundamentals of A.I. into all subject areas.

There was a time when educators feared that the use of handheld calculators would encourage students to cheat on math homework and ruin their mathematical reasoning skills because students would lose the ability to do mental math. Similarly dire predictions were made about the spell-check functions on word processors and, later, by programs that corrected grammar and usage errors in student essays. The latest concern over how technology might impair student learning is generative artificial intelligence (AI), including Chat GPT, HayoAI, Tailor, and many others. Teachers have legitimate concerns about the impact of AI on learning, with AI programs writing student essays, lab reports, and responding to test questions in a broad array of subjects. In a recent survey, 56 percent of students admit to using AI to craft answers on assignments despite the growing efforts of schools to curtail student’s use of it. 

El informe ofrece un análisis detallado sobre cómo los estudiantes universitarios en Estados Unidos están adoptando herramientas de inteligencia artificial, especialmente ChatGPT, y cómo esta tendencia influye en la preparación de la futura fuerza laboral.

Según el informe, más de un tercio de los jóvenes de 18 a 24 años en EE. UU. utilizan ChatGPT, y aproximadamente una cuarta parte de sus interacciones con la herramienta están relacionadas con el aprendizaje, la tutoría y el trabajo académico. Los estudiantes emplean ChatGPT para iniciar trabajos y proyectos, resumir textos extensos, explorar temas, revisar escritos y obtener ayuda en programación.

While Artificial Intelligence has already introduced significant disruption to educational systems, quantum computing stands to exponentially amplify these shifts. Quantum-AI Systems, which leverage probabilistic computing to process high-dimensional data and explore vast solution spaces in parallel, may redefine how learning environments adapt, how decisions are optimized, and how institutional systems are structured. This convergence of quantum computing and AI introduces fundamentally new computational logics that challenge conventional models of curriculum design, assessment, and pedagogical agency.

La IA Generativa tendrá un impacto notorio en los empleos de oficina dentro de los sectores de servicios altamente calificados, que por lo general ocupan personas con educación universitaria. A diferencia de las oleadas anteriores de las tecnologías digitales, que principalmente permitían acelerar tareas rutinarias o hacer predicciones mediante el reconocimiento de patrones de datos, la capacidad de la IA generativa para sintetizar y generar ideas y contenidos se cruza con una parte significativa de las tareas que requieren las ocupaciones de oficina.

According to Inside Higher Ed and Generation Lab's 2024 Student Voice survey, 31% of undergraduates don't know when or how to use Generative AI tools like ChatGPT in their coursework. Meanwhile, EDUCAUSE's 2025 AI Landscape Study shows that although 57% of colleges now consider AI a strategic priority, fewer than 40% have formal use policies—and most of those focus only on plagiarism rather than broader curricular or community implications.

These gaps come at a time when student adoption of AI is near-universal. The Higher Education Policy Institute’s 2025 Student Generative AI Survey , a UK-based survey, found that 92% of students now use AI in some form, up from 66% in 2024, with 88% reporting they’ve used Generative AI for assessments. Another survey by the Digital Education Council found 86% of students already using AI in their studies. Yet the same surveys reveal that only 36% of students have received support from their institution to develop AI skills, despite overwhelming student belief that these skills are essential.

En el contexto de la educación superior, la implementación de tecnologías avanzadas como las redes de sensores, sistemas multi-robot y algoritmos de aprendizaje de refuerzo distribuido promete transformar radicalmente la experiencia educativa. Este trabajo explora cómo la coordinación descentralizada y autónoma de estos agentes puede optimizar el entorno de aprendizaje, proporcionando una experiencia personalizada y eficiente para estudiantes y docentes. Utilizando estudios de caso y evidencias científicas, analizamos el impacto de estas tecnologías en la mejora del rendimiento académico y la satisfacción estudiantil. En un mundo donde la educación disruptiva es esencial para enfrentar los desafíos del siglo XXI, integrar inteligencia artificial y robótica en el aula se presenta no solo como una innovación deseable, sino como una necesidad imperativa para alcanzar niveles superiores de aprendizaje y adaptación.

El salón de clases es un entorno extraordinariamente rico en producción de datos, aunque la mayoría de ellos permanecen invisibles bajo nuestra concepción tradicional de lo que constituye "información educativa relevante". Cada pregunta formulada por un estudiante, cada duda expresada con timidez, cada momento de entusiasmo colectivo ante un descubrimiento compartido, cada patrón recurrente de participación o silencio, todos ellos representan fragmentos de información potencialmente valiosos que, sin embargo, suelen desvanecerse en el flujo constante de la dinámica escolar, sin dejar más huella que una impresión difusa en la memoria del docente.

Los comentarios espontáneos de un estudiante durante un debate pueden revelar procesos cognitivos que ningún examen convencional lograría detectar. Las preguntas formuladas durante una explicación pueden señalar patrones colectivos de comprensión o confusión que trascienden las barreras individuales. Los cambios sutiles en el tono de voz o en la postura corporal de un grupo ante determinados contenidos pueden ser indicadores tempranos de compromiso o desconexión con el material presentado. Todas estas "micro-señales" constituyen datos educativos sumamente valiosos que tradicionalmente se han perdido en la inmediatez del momento, o que, en el mejor de los casos, han quedado relegados a anotaciones personales no sistematizadas del docente.

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).

As the labor market evolves and as generative AI impacts industries, students and employers believe microcredentials play an important role in career success, according to a new report from Coursera.

Nearly 9 in 10 students view microcredentials as key to job success, and 96 percent of students favor the inclusion of GenAI credentials in degree programs, according to Microcredentials Impact Report 2025: Insights from Students and Employers.

AI also brings its share of challenges, particularly when it comes to assessing student performance. One of the most common questions educators ask me is, “How can we stop students from using AI to cheat?” I have to admit that it’s fascinating to me that we are suddenly so interested in addressing cheating. Research from Stanford University School of Education shows that the prevalence of cheating among U.S. high school and college students is nothing new. The study found that nearly 70 percent of U.S. high school and college students regularly engaged in cheating long before the emergence of generative AI. Other studies have shown similar results. 

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.

The new publication includes guidance for students using AI in their college studies and offers practical advice about preparing for careers that require AI knowledge and skills. The content was developed with input from consulting scholars and students in 14 countries.

Students, faculty and staff at 1,900 colleges and universities around the world accessed the first Student Guide to AI when it was released in August 2024. Since then, the guide’s website has provided information to more than 36,000 users in 139 countries. The second publication supplements the first, and both are useful resources for students, faculty and staff who want to learn about AI.

“By providing a free resource written in a way all students can access, we hope to increase AI literacy and support students as they adapt to these rapidly changing technologies,” says Elon University President Connie Book. “From the outset, we knew that a second publication would be necessary, with rapid advances in AI changing the learning landscape. This current version will be especially useful as colleges and universities prepare for the upcoming academic year.”

As a new type of artificial intelligence, ChatGPT is becoming widely used in learning. However, academic consensus regarding its efficacy remains elusive. This study aimed to assess the effectiveness of ChatGPT in improving students’ learning performance, learning perception, and higher-order thinking through a meta-analysis of 51 research studies published between November 2022 and February 2025.

The results indicate that ChatGPT has a large positive impact on improving learning performance (g = 0.867) and a moderately positive impact on enhancing learning perception (g = 0.456) and fostering higher-order thinking (g = 0.457). The impact of ChatGPT on learning performance was moderated by type of course (QB = 64.249, P < 0.001), learning model (QB = 76.220, P < 0.001), and duration (QB = 55.998, P < 0.001); its effect on learning perception was moderated by duration (QB = 19.839, P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (QB = 7.811, P < 0.05) and the role played by ChatGPT (QB = 4.872, P < 0.05).

This study suggests that:

1. Appropriate learning scaffolds or educational frameworks (Bloom’s Taxonomy) should be provided when using ChatGPT to develop students’ higher-order thinking.
2. The broad use of ChatGPT at various grade levels and in different types of courses should be encouraged to support diverse learning needs.
3. ChatGPT should be actively integrated into different learning modes to enhance student learning, especially in problem-based learning.
4. Continuous use of ChatGPT should be ensured to support student learning, with a recommended duration of 4–8 weeks for more stable effects.
5. ChatGPT should be flexibly integrated into teaching as an intelligent tutor, learning partner, and educational tool.

Finally, due to the limited sample size for learning perception and higher-order thinking, and the moderately positive effect, future studies with expanded scope should further explore how to use ChatGPT more effectively to cultivate students’ learning perception and higher-order thinking.

La irrupción de la Inteligencia Artificial Generativa (GenAI) en el ámbito de la escritura, ya sea académica, literaria o profesional, y en particular la capacidad de sistemas como ChatGPT o Gemini para generar texto y asistir en diversas tareas de redacción ha puesto sobre la mesa una pregunta esencial:

¿Qué significa ser autor en la era de la máquina que "escribe"?

AI systems are no longer just specialized research tools: they’re everyday academic companions. As AIs integrate more deeply into educational environments, we need to consider important questions about learning, assessment, and skill development. Until now, most discussions have relied on surveys and controlled experiments rather than direct evidence of how students naturally integrate AI into their academic work in real settings.

To address this gap, we’ve conducted one of the first large-scale studies of real-world AI usage patterns in higher education, analyzing one million anonymized student conversations on Claude.ai.

La reciente euforia en torno a la IA ha llevado a algunos a presentarla como una panacea para los desafíos educativos, una herramienta capaz de resolver problemas ancestrales y, en algunos discursos, incluso de optimizar o sustituir la labor del docente. Sin embargo, esta perspectiva corre el riesgo de obviar la naturaleza intrínsecamente humana del proceso educativo. Reducir la enseñanza y el aprendizaje a meros algoritmos y datos pasa por alto las dimensiones más profundas y esenciales que nutren el desarrollo integral de los estudiantes.

La educación, en su esencia, trasciende la simple transmisión de información. Se fundamenta en la creación de vínculos significativos entre educadores y alumnos, en el cultivo del pensamiento crítico y la creatividad, en el fomento de la empatía, la ética y los valores que guían la convivencia social. Estos pilares del desarrollo humano florecen en un entorno de interacción genuina, donde la comunicación va más allá de la instrucción programada y se nutre de la comprensión mutua, la observación atenta y la respuesta sensible a las necesidades individuales dentro de un contexto social.

In recent years we have witnessed numerous crises affecting learning and teaching in higher education institutions to varying degrees. Various indexes on future skills, jobs and risks indicate that these crises will not disappear any time soon.

Universities worldwide are grappling with different types of crises stemming from financial constraints and budget cuts, wars and armed conflicts, natural calamities, political pressures, legal impediments, unequal access to technological abilities, cybersecurity risks, unsafe campus environments, refugees and displaced populations, climate change and interconnected multiple crises or ‘polycrises’.