While most faculty are now struggling with students’ reliance on ChatGPT, as a scholar who has studied disruptive technologies, as well as their impact on jobs, I’m here to argue that student usage of ChatGPT should be the least of faculty members’ concerns. Make no mistake: At the end of this decade, i.e., 2030, AI will be solely teaching a notable percentage of courses across the globe. (Don’t believe me? Five years ago, I wrote that by 2024 AI would replace software engineers—a prediction that raised skeptical eyebrows. Well, I hate to say I told you so: AI is beginning to replace coders.)

The COVID outbreak may be seen as a “black swan” occurrence, the arrival of artificial intelligence (AI) had been anticipated for decades. Indeed, thirteen years before the release of ChatGPT 3.5, Apple had introduced Siri, its voice activated personal assistant which relies on machine learning technology. Given the ubiquity and popularity of Apple products, this impressive but milder form of AI should have been the perfect herald for what was to follow. Despite this, by the fall of 2023, educators were astonished and deeply worried by the capabilities of generative AI, viewing it as a threat to academic integrity and the teaching profession.

This reaction underscores a deeper issue: the resistance to AI in education is not truly about learning. It reflects a reluctance to re-evaluate the traditional roles of educators and to embrace the opportunities AI offers to enhance the learning experience.

AI can make life too easy. As educators, a big concern is our students being able to produce a well-organized essay, backed up with strong evidence, all by putting the right prompt into a generative AI (GenAI) tool. But how are they going to develop the skills they need to make sense of the world, to express their own thoughts, and to set themselves apart from everyone else with ChatGPT so readily available?

ChatGPT is an impressive mimicry of a fully-conscious conversational partner. Perhaps too impressive?

La innovación educativa no es un concepto nuevo. Desde los inicios de la educación masiva, han existido pioneros y pioneras que han buscado formas alternativas de enseñar, cuestionando el statu quo y proponiendo métodos más efectivos y significativos. Sin embargo, lo que distingue el momento actual es la creciente conciencia social de que la escuela necesita transformarse. Ya no se trata de iniciativas aisladas o de educadores visionarios trabajando en solitario; estamos presenciando un movimiento global hacia una educación más dinámica, inclusiva y relevante.

El desafío que enfrentamos no es solo tecnológico o metodológico; es fundamentalmente un desafío de concepciones y decisiones. Debemos estar dispuestos a cuestionar nuestras suposiciones, a experimentar con nuevas ideas y a abrazar el cambio. La educación del futuro debe ser capaz de desarrollar en los estudiantes no solo conocimientos, sino también habilidades críticas, creatividad, capacidad de adaptación y una mentalidad de aprendizaje permanente.

Especialistas en Tecnología Educativa, entre ellos Alejandro Artopoulos, advierten sobre las consecuencias negativas para la inclusión digital y el aprendizaje. En lugar de eliminar los dispositivos, proponen ideas para integrarlos de una manera pedagógica y consciente porque, coinciden, los celulares pueden ser herramientas clave para el desarrollo de competencias digitales y la ciudadanía crítica, aspectos esenciales en la educación del siglo XXI.

We live in the age of robots. Thanks to advances in AI, it is now theoretically possible to, say, make an entire feature-length film without hiring any humans to write a screenplay, act or compose the score.

Naturally, AI has found its way into just about every area of modern life, including academia. Their use at colleges and universities has not been entirely free of controversy, but AI and automation have a number of practical applications in the academic library. Indeed, these tools have the potential to transform academic librarianship for the better.

La universidad del futuro se enfrenta a un desafío sin precedentes: adaptarse a un entorno en constante cambio, impulsado por la tecnología disruptiva y la inteligencia artificial (IA). En lugar de resistirse a estos cambios, la institución debe aprender a reinventarse a sí misma. Esto implica adoptar una mentalidad de auto-innovación, que permita a los estudiantes y profesores diseñar sus propias rutas de adaptación y renovación permanente.

La auto-innovación incremental se refiere a la mejora continua de los procesos y métodos existentes. Por ejemplo, un profesor de matemáticas puede utilizar herramientas de IA para personalizar la enseñanza y hacerla más interactiva, utilizando plataformas de aprendizaje en línea que se adaptan a las necesidades individuales de cada estudiante.

Por otro lado, la auto-innovación radical (disruptiva), implica la creación de nuevos modelos y paradigmas que revolucionen la forma en que se enseña y se aprende. Un ejemplo de esto es la implementación de programas de aprendizaje automático que permiten a los estudiantes crear sus propios cursos y contenidos, utilizando tecnologías como la realidad virtual y aumentada.

La IA y la automatización pueden ayudar a los estudiantes a desarrollar habilidades como la resolución de problemas complejos, la creatividad y la colaboración. Por ejemplo, un estudiante de ingeniería puede utilizar herramientas de IA para diseñar y simular prototipos de productos, lo que le permite experimentar y aprender de manera más efectiva.

A private school in London is opening the UK's first classroom taught by artificial intelligence instead of human teachers. They say the technology allows for precise, bespoke learning while critics argue AI teaching will lead to a "soulless, bleak future".

Hoy en día, resulta imposible imaginar un mundo sin tecnología. Desde los teléfonos móviles hasta las redes sociales, la tecnología ha transformado todos los aspectos de nuestra vida, incluyendo, por supuesto, la educación. Pero, ¿realmente la tecnología en las Aulas mejora la tecnología la forma en que aprendemos?

En primer lugar, es importante entender qué tipo de tecnología estamos hablando. En las aulas, nos referimos principalmente al uso de ordenadores, tablets, pizarras digitales e incluso inteligencia artificial para apoyar el aprendizaje de los estudiantes. Estos recursos deberían facilitar la enseñanza y ayudar a los alumnos a adquirir habilidades necesarias para el siglo XXI. Sin embargo, en la práctica, no todo el mundo está de acuerdo con este enfoque.

Instead, we should think of them as the fundamentals.

As I’ve written previously, as a teacher of writing, I cannot outsource giving writing feedback to something that cannot read. Reading student writing is a fundamental part of teaching because I absolutely must know what students are doing to be able to help them develop. In fact, reading their work is not sufficient by itself. I must also have a conversation with them about my impressions of what they’ve written. This is the work.

Using Generative Artificial Intelligence (GenAI) tools such as ChatGPT, Gemini, or CoPilot as intelligent assistants in instructional design can significantly enhance the scalability of course development. GenAI can significantly improve the efficiency with which institutions develop content that is closely aligned with the curriculum and course objectives. As a result, institutions can more effectively meet the rising demand for flexible and high-quality education, preparing a new generation of future professionals equipped with the knowledge and skills to excel in their chosen fields. In this article, we illustrate the uses of AI in instructional design in terms of content creation, media development, and faculty support. We also provide some suggestions on the effective and ethical uses of AI in course design and development. Our perspectives are rooted in medical education, but the principles can be applied to any learning context.

The federal government has committed to a national plan to ban children from social media.

Details are still scarce. Prime Minister Anthony Albanese has acknowledged that “no government is going to be able to protect every child from every threat, but we have to do all we can”.

But banning children from social media isn’t going to fix the problem of online harms faced by young people – it’s only going to put the problem on pause. That’s because when children reach an age when they can use social media, they are still going to face many of the same issues.

Actualmente varios gobiernos regionales en España y de otros países europeos están  reglamentado la prohibición del uso de los teléfonos móviles, smartphones o celulares en su sistema escolar (Albalad, 2023) con el argumento de proteger a los niños y adolescentes de los efectos perniciosos provocados por el sobreuso de la tecnología móvil.

There are significant implications of artificial intelligence (AI) for education, teaching and learning, and for teachers’ roles and competencies. Indeed, AI can process vast amounts of information and text far beyond any human capacity and can produce new content across the range of symbolic representations of human thinking, identify patterns in data presented in various formats, and can facilitate human decision-making by predictive analyses. Emerging practices in the use of AI in education clearly demonstrate the potential of AI to enable new forms of teaching, learning and education management and enhance learning experiences and support teacher tasks.

However, AI can pose significant risks to students, the teaching community, education systems and society at large. AI may threaten human agency, intensify climate change, violate data privacy, deepen long-standing systemic inequalities and exclusion, and lead to new forms of discrimination. In education, AI can reduce teaching and learning processes to calculations and automated tasks in ways that devalue the role and influence of teachers and weaken their relationships with learners. It can narrow education to only that which AI can process, model and deliver. Finally, it can also exacerbate the worldwide shortage of qualified teachers through disproportionate spending on technology at the expense of investment in human capacity development.

The use of AI in education therefore requires careful consideration, including an examination of the evolving roles teachers need to play and the competencies required of teachers to make ethical and effective use of AI. Teachers are the primary users of AI in education, and they are expected to be the designers and facilitators of students’ learning with AI, the guardians of safe and ethical practice across AI-rich educational environments, and to act as role models for lifelong learning about AI. To assume these responsibilities, teachers need to be supported to develop their capabilities to leverage the potential benefits of AI while mitigating its risks in education settings and wider society.

David Game College in London, UK, is planning to allow students around the age of 15 to use  AI tools before taking standardized exams next year. This initiative, despite concerns from experts about the limitations of AI compared to human teachers, aims to support both students who struggle to keep up and those who excel.

Para aplicar un enfoque creativo y disruptivo utilizando inteligencia artificial (IA) en una comunidad de universidades (interuniversidades) se puede diseñar un escenario innovador basado en la analogía de sinapsis algorítmicas y sinergias neuronales. Este enfoque se inspira en la estructura del cerebro humano, donde las neuronas se conectan a través de sinapsis para procesar y transmitir información de manera eficiente y adaptativa. Del mismo modo, una red de universidades puede funcionar como un ecosistema inteligente que optimiza el aprendizaje y la colaboración mediante el uso de IA avanzada.

Las sinergias neuronales en la inteligencia artificial (IA) se refieren a la manera en que los sistemas de IA se inspiran en el funcionamiento del cerebro humano para mejorar su eficiencia y capacidad de aprendizaje. La sinapsis, que es la conexión entre las neuronas en el cerebro, ha sido un modelo para el diseño de redes neuronales artificiales, que son la base de muchos algoritmos de IA.

Este libro surge con la intención de revisar las porosidades y vínculos entre la didáctica y la tecnología, en clave crítica y disidente. Aborda discusiones sobre las transformaciones digitales, los algoritmos, los desarrollos tecnológicos vertiginosos y los multiterritorios donde habitan el aprendizaje y la enseñanza.

Esta obra se propone revisitar el campo de la didáctica desde los
aportes de un grupo de referentes y especialistas que interrogan, de manera implícita o explícita, el estatus epistemológico de la disciplina, en escenarios polifónicos y en el marco de profundas transformaciones sociales y educativas.

Los atravesamientos tecnológicos, la expansión digital y las propuestas híbridas cada vez más presentes en la enseñanza de nivel superior dan cuenta de la necesaria reconceptualización y revisión del campo de la didáctica y de las encrucijadas y tensiones entre la didáctica y la tecnología en los nuevos escenarios educativos.

Las indagaciones que componen este libro se proponen explorar
algunas cuestiones que suponen opciones no instrumentales en los
vínculos entre la didáctica y la tecnología y que plantean desafíos importantes para pensar la enseñanza. Al involucrar aspectos sociales, culturales, políticos, epistémicos y comunicacionales, el análisis reclama un debate profundo acerca de los sentidos y las posibilidades de creaciones educativas en diálogo con atravesamientos tecnológicos cada vez más complejos.

La didáctica, tradicionalmente vinculada con propuestas de enseñanza articuladas con diseños curriculares más o menos prescriptivos, se ve interpelada por los entornos expansivos, las nuevas formas de corporeidad, las promesas, las oportunidades y las constantes demandas de la cultura digital. Esta mirada requiere de posicionamientos críticos, fundamentados y reflexivos para llevar adelante adaptaciones o cambios en los modos de concebir los espacios de enseñanza y de aprendizaje con fuerza epistemológica y comprometidos con lo social.

The proliferation of artificial intelligence (AI) technologies and chatbots has the potential to significantly reshape higher education. It is now imperative for stakeholders in this sector to grasp the fundamental aspects of AI technologies and understand their implications. This paper not only introduces basic AI concepts but also explains their specific applications and relevance in the higher education context. Moreover, it outlines the prospects of using AI technologies and chatbots to boost student engagement, presenting a synthesis of the opportunities available. Concurrently, we discuss the concerns and challenges associated with integrating AI into higher education settings. Several articles included in this special issue explore these opportunities and challenges from diverse viewpoints and within various contexts, across countries such as Australia, the United Kingdom, Vietnam, Cyprus, and GCC nations. Finally, we propose several avenues for future research aimed at enhancing student engagement through AI, charting a path forward for empirical evidence and practical application of AI and chatbots in enhancing student engagement.

When Generative AI (a version of artificial intelligence that focuses on creating new content) rapidly became more prevalent in educational settings, it sparked fear and skepticism among some educators. Many saw it primarily as a high-tech cheating tool, a way for students to outsource their thinking and writing. But as the dust settled, a more nuanced and exciting possibility emerged: AI as a powerful tool to enhance students’ metacognition—the ability to think about one’s own thinking processes.

US Universities are engaging with AI in ways beyond anything I see in the UK, Europe and elsewhere. I’ve written about Harvard before but Yale seems like the strongest in intent, widest in access and deepest in enabling research, teaching and learning. Well funded at $150 million, they understand the need for the necessary IT infrastructure.

What really matters is top-down management intent. Scott Strobel has been the Provost of Yale since 2020. As a successful science researcher and Caltech graduate he understands the technology and has sought, through his Yale Task Force on Artificial Intelligence, recommendations that were delivered in June. It has taken only two months to get a plan and funding in place. That is impressive.