La tecnología no es sólo una herramienta, sino un producto social, que está lejos de ser neutro. El diseño de cualquier tecnología es un acto moral. De hecho, muchas de las más avanzadas tecnologías poseen una moral, en el sentido que se comportan de acuerdo a unas normas inscritas en su diseño (esto es muy claro con los sistemas basados en IAs y data). Defender la idea de una tecnología neutra tan solo condicionada por el uso que hagamos de ella es una manera de ocultar (conscientemente o no) este hecho y las responsabilidades que implica. Y es además algo poco útil porque nos dificulta aclarar las opciones éticas y las compensaciones que a menudo requieren muchas tecnologías y, por lo tanto, las políticas y regulaciones que deben diseñarse (Floridi, 2023).

AI is forcing us to reimagine Education as a vehicle for democratizing thinking and knowing. There is no denying that. About 40% of the world’s population is under 24. If schools fail to prepare this generation of youth for the age of thinking machines, the consequences on social and economic peace may be dire.

The ChatGPT Voice Assistant is revolutionizing the way teachers interact with technology, making classroom management, lesson planning, and student engagement more efficient and interactive. Here’s how educators can harness the power of this AI teaching assistant to enhance their teaching experience.

Covid helped speed up a digital revolution in higher education that had previously been forecast to occur around 2030. A decade’s worth of change was ushered in practically overnight. Though the speed of this transition is admirable, given its necessity, there is still much to be done to make sure technology works for the people who navigate universities every day – students, teachers, researchers and administrative staff. This is what my colleagues and I at EY set out to investigate through our report into what each of these stakeholder groups need from technology in order to thrive.

En esta era digital, donde la tecnología avanza a un ritmo vertiginoso, el sistema educativo se enfrenta a un desafío crucial: ¿cómo reaccionar ante estos cambios cada vez más rápidos y constantes? Es evidente que la educación no sigue ni ha seguido, a diferencia de otros ámbitos, las cada vez más aceleradas y disruptivas transformaciones tecnológicas, que a su vez -para decirlo brevemente- nos cambian la vida.

Esta brecha plantea interrogantes fundamentales que requieren una profunda reflexión y una respuesta contundente de parte de todos los actores involucrados en el proceso educativo. La resistencia y pasividad al cambio ya no es una opción viable en un mundo cada vez más digitalizado y tecnológico, donde mantenerse estático deja a los estudiantes desprovistos de las habilidades y conocimientos necesarios para enfrentar los desafíos del siglo XXI.

Sal Khan, fundador y CEO de Khan Academy, cree que la IA podría desencadenar la mayor transformación positiva de la Educación jamás vista. Comparte las oportunidades que ve para que estudiantes y educadores colaboren con herramientas de IA, incluido el potencial de un Tutor Personal IA para cada estudiante y un Asistente de Enseñanza IA para cada maestro. Se demuestra algunas nuevas características interesantes para su chatbot educativo, Khanmigo.

Since 2019, the presence of Deepfakes - hyperrealistic AI-generated video, photo, and audio forgeries - has surged by 550%, reaching a staggering 95,820 documented deepfake videos in 2023 alone.

This blurring of reality requires a new approach to vetting if online content is real or fake. This guide is designed to build student awareness of the presence and impact of deepfakes, while providing key discussion topics on the ethics of AI-generated content.

We still assess students based predominantly on written, terminal assessments that focus on narrow ranges of knowledge and an even narrower skills base. We get kids to memorize a formula and regurgitate ‘good’ answers into it. And I’m not talking about mathematical formula – a distressing amount of my teaching has been about how to structure exam responses. Not construct an effective argument or order your thoughts – no, just get an answer to a specific question type in the format that best matches the aims of the exam board. Students aren’t learning anything useful here; they’re just brute-forcing results using the quickest and most direct method.

True experiments are a critical research methodology because the sample groups must be assigned randomly between control or experimental groups, and all variables except the one being studied are controlled, in order to best determine causality. We’re designing true experiments that explore prescriptive questions about the ways the technology should be deployed as an instructional tool — this is a key element of advancing teaching and learning in higher ed. When it comes to investigating generative AI tools through an SoTL Research framework, essential questions combine elements that are discipline specific with more general considerations of the student experience.

In the denouement of the COVID-19 pandemic, talk of a return to “normalcy” in higher education belies the great challenges and
ongoing disruptions that yet lie ahead for many institutions. Public perceptions of the value of postsecondary education continue
their downward slide, placing institutions in the position of having to demonstrate their worth and find solutions to declining
enrollments. Data and analytics capabilities continue to evolve, introducing new opportunities and new risks to the institution. Chief among these capabilities, Generative AI promises to change teaching and learning in ways many of us have yet to fully understand or prepare for.

For this year’s teaching and learning Horizon Report, expert panelists’ discussions highlighted and wrestled with these present and looming challenges for Higher Education. This report summarizes the results of those discussions and serves as one vantage point on where our future may be headed. This project was grounded in a modified Delphi methodology that seeks to elevate the collective perspectives and knowledge of a diverse group of experts, and the panelists’ activities were facilitated using tools adapted from the Institute for the Future.

GenAI can democratize Education, tailor learning experiences to individual needs and bridge disparity gaps, giving it potential as an immense and versatile student success tool. But educators must take the helm of research and development, steering the integration of AI with intention and expertise. Following are six areas around which to focus action.

In a world where knowledge is power and innovation drives progress, the role of universities in society stands at a pivotal crossroads. The traditional image of ivory towers of academia is changing as we navigate the complexity of the 21st century. Today, universities are not only centers of learning and research but dynamic hubs of innovation, collaboration and societal impact.

En un mundo cada vez más interconectado, el concepto de cultura adquiere nuevas dimensiones y complejidades. Tradicionalmente, la cultura ha sido vista como un conjunto homogéneo de prácticas y valores compartidos por una sociedad. Sin embargo, dentro de estas culturas dominantes existen grupos menores, conocidos como subculturas, que poseen sus propias identidades. Vamos a ver cómo las subculturas, lejos de ser meros fragmentos aislados, se integran y transforman la cultura más amplia, influyendo en aspectos tan variados como la moda, el lenguaje y las normas sociales.

La importancia de la educación digital radica en su facultad para capacitar a las generaciones presentes y futuras para prosperar en un mundo moldeado por la tecnología. Al abrazar esta revolución educativa, abrimos las puertas a la innovación, la creatividad y el progreso humano. Sin embargo, solo a través de un compromiso colectivo y una visión inclusiva podemos aprovechar plenamente el potencial transformador de la Educación Digital.

Este reporte aspira a presentar nueve dimensiones sobre la Educación Digital en el siglo XXI. Haciendo un recorrido útil a través de la historia, definiciones, comentarios de expertos, así como casos de éxito que existen en la educación superior. Su intención es ser una guía que oriente desde un enfoque práctico a las universidades que estén buscando entrar a este modelo educativo o a aquellas que tienen experiencia previa.

La IA, por avanzada que sea, no puede replicar la comprensión empática y la conexión humana que un profesor puede ofrecer. Un algoritmo puede analizar patrones de aprendizaje y adaptar el material de estudio a las necesidades de un estudiante, pero no puede entender las sutilezas emocionales de ese estudiante ni ofrecer el apoyo humano que a menudo es crucial para superar los desafíos. La tecnología no puede caminar al lado de un estudiante en su viaje de crecimiento personal, no puede ver la chispa en los ojos de un niño cuando comprende un concepto complicado por primera vez, ni puede ofrecer un aliento genuino que pueda cambiar la trayectoria de la vida de una persona.

Imagine a classroom in 2030 where artificial intelligence (AI) handles the grading, providing instant feedback, and tailoring learning experiences for every student. This futuristic vision is closer than you might think, and it promises to revolutionize education in ways we are just beginning to explore. Let’s dive into the exciting possibilities of AI grading and its potential impact on education.

The evolving role of teachers into guides or facilitators is gaining traction, yet this transition does not come naturally to everyone.

Many of us were drawn to this profession because of our passion for the subject matter and direct interaction with students. While some schools have successfully adopted this new model, it’s important to recognise that these changes are often more palatable when teachers choose to engage with them, rather than having change imposed from the outside.

En el ámbito universitario, la investigación se transforma con la ayuda de agentes multi-documentos que pueden analizar grandes conjuntos de datos, identificar patrones y tendencias, y generar nuevas hipótesis. La colaboración entre estudiantes y agentes especializados en diferentes áreas de conocimiento impulsa la innovación y acelera el ritmo de los descubrimientos.

La visión de Farnos, centrada en la personalización y la accesibilidad, se materializa en un ecosistema educativo donde la IA no reemplaza al educador, sino que lo empodera con herramientas que le permiten dedicar más tiempo a la interacción humana y al desarrollo integral de cada estudiante. El aprendizaje autónomo se fortalece con sistemas de recomendación inteligentes que guían a los estudiantes hacia los recursos más relevantes para sus necesidades e intereses.

Effective AI integration involves the creation of bespoke, individual prompts or applications tailored to the unique needs of each person, task, and sometimes even the specific tool being used. This level of customization ensures that AI directly supports the goals and objectives of the school and its stakeholders. It’s not about preset libraries of prompts or generic solutions.

There are risks, of course, but AI safety is much better ensured by embedding AI literacy into the fabric of your school than by relying on an assembly or a few explicit lessons. The AI landscape is constantly shifting, so the more sustainable approach is to foster a culture of constant conversations around AI that equips students, staff and parents with the knowledge and skills necessary to make informed decisions about its use.

Academics across the country are talking about the reading problems they are seeing among traditional-age students. Many, they say, don’t see the point in doing much work outside of class. Some struggle with reading endurance and weak vocabulary. A lack of faith in their own academic abilities leads some students to freeze and avoid doing the work altogether.

And a significant number of those who do the work seem unable to analyze complex or lengthy texts. Their limited experience with reading also means they don’t have the context to understand certain arguments or points of view.

En una universidad que adopta un enfoque de educación disruptiva y utiliza inteligencia artificial (IA), la carga cognitiva desempeña un papel crucial en el proceso educativo. La carga cognitiva se refiere a la cantidad de esfuerzo mental requerido para completar una tarea o actividad de aprendizaje.

En este contexto, la IA se emplea para personalizar la experiencia educativa de cada estudiante, lo que implica adaptar el contenido del curso, los recursos y las actividades según las necesidades individuales y preferencias de aprendizaje. Esto busca reducir la carga cognitiva al proporcionar un enfoque más centrado y relevante para cada estudiante, optimizando así su capacidad de absorción y retención de información.

The rise of generative AI tools like ChatGPT, Copilot, Claude, Gemini, DALL-E, and Meta AI has created a pressing new challenge for educators: defining academic integrity in the age of AI.  

As educators and students grapple with what is allowed when using generative AI (GenAI) tools, I have compiled five tips to help you design or redesign academic integrity statements for your syllabus, assignments, exams, and course activities.  

The problem is not that universities are lacking in innovation, but rather that they suffer from a poverty of imagination of what that innovation might be. “This is an existential moment for universities,” writes the philosopher of higher education Ronald Barnett. “All systems of higher education across the world are moving inexorably in the direction of the marketized university. Consequently, the pool of ideas through which the university is comprehended is shrinking.” Indeed, the main existential crisis facing the university is a poverty of ideas about what universities can become. We must look elsewhere, more broadly and with greater imagination, for potential sources of innovation. 

La neurobiología en procesos de inteligencia artificial general se refiere a la investigación y comprensión de cómo funciona el cerebro humano para inspirar y mejorar los algoritmos y modelos utilizados en la inteligencia artificial (IA). Esto implica estudiar las estructuras neuronales, las conexiones sinápticas y los procesos cognitivos que subyacen a la inteligencia humana. Al comprender mejor cómo funciona el cerebro, los científicos pueden diseñar sistemas de IA más sofisticados que imiten de manera más efectiva las capacidades cognitivas humanas, como el razonamiento, el aprendizaje y la toma de decisiones.