The digital divide describes disparities in access to and usage of digital tooling between social and economic groups. Emerging GAI
tools, which strongly affect productivity, could magnify the impact of these divides. However, the affordability, multi-modality, and multilingual capabilities of these tools could also make them more accessible to diverse users in comparison with previous forms of digital tooling. In this study, we characterize spatial differences in U.S. residents’ knowledge of a new generative AI tool, ChatGPT, through an analysis of state- and county-level search query data. In the first six months after the tool’s release, we observe the highest rates of users searching for ChatGPT in West Coast states and persistently low rates of search in Appalachian and Gulf states.

Counties with the highest rates of search are relatively more urbanized and have proportionally more educated, more economically advantaged, and more Asian residents in comparison with other counties or with the U.S. average. In multilevel models adjusting for socioeconomic and demographic factors as well as industry makeup, education is the strongest positive predictor of rates of search for generative AI tooling. Although generative AI technologies may be novel, early differences in uptake appear to be following familiar paths of digital marginalization.

EDUCAUSE is helping institutional leaders, IT professionals, and other staff address their pressing challenges by sharing existing data and gathering new data from the higher education community. This report is based on an EDUCAUSE QuickPoll. QuickPolls enable us to rapidly gather, analyze, and share input from our community about specific emerging topics.

Partimos de la premisa de que la novedad de la inteligencia artificial en la educación -y en muchos o todos los ámbitos de aplicación- eventualmente pasará. Esta transición se dará no solo por la vorágine misma de estos tiempos, sino en especial por varias razones interconectadas, y a la vez relacionadas con el avance de esta tecnología en general.

AI: two letters on the tip of everyone’s tongue. But in the midst of cultural conversations around its potential for progress and innovation, how do we ensure we use it without losing the things that make us human? In this thoughtful look at the past, present and future of AI, Dr. George Siemens talks about the intersection of people, nature and technologies — and what it means for our sense of connection, community, and self-identity. 

La realidad postecnológica es un concepto que describe una etapa de la humanidad donde la tecnología ya no es vista simplemente como una herramienta o una extensión de nuestras capacidades, sino que se ha integrado tan profundamente con nuestras vidas que la distinción entre tecnología y humanidad comienza a difuminarse.

En el contexto de la educación superior, la IAGen puede potenciar la investigación mediante la generación automática de hipótesis, el análisis de grandes conjuntos de datos y la identificación de patrones complejos. Investigadores como Yoshua Bengio, Geoffrey Hinton y Yann LeCun, pioneros en el campo del aprendizaje profundo, han demostrado cómo los algoritmos de AGI pueden aplicarse con éxito en diversas disciplinas, desde la medicina hasta la física.

En la educación superior, se han utilizado diversos algoritmos para mejorar la experiencia de aprendizaje de los estudiantes. Algunos de los algoritmos destacados incluyen:

La inteligencia artificial (IA) está redefiniendo las estructuras de enseñanza y aprendizaje que conocíamos. Este cambio no se limita a la incorporación de nuevas herramientas tecnológicas en el aula, sino que se extiende a cómo concebimos la educación y su capacidad para adaptarse a las necesidades individuales de cada estudiante.

La promesa de la IA en la educación es doble: personalización a gran escala y democratización del acceso al conocimiento. El aprendizaje marca el comienzo de una era donde la educación para el futuro se vuelve más inclusiva, adaptativa y eficaz.

According to a new report from leading global education company McGraw Hill, educators believe the biggest obstacles to student success in school are tied to challenges outside the classroom, including mental health and behavior issues. While some educators worry that technology and artificial intelligence (AI) might have a negative effect in these areas, many see the positive impact of AI on students' ability to learn in their preferred languages, improved grades and career readiness.

En un mundo donde la primera impresión puede definir el futuro de nuestras relaciones profesionales y personales, la habilidad de comunicarse efectivamente se convierte en una herramienta esencial. No es solo lo que decimos, sino cómo lo decimos lo que puede marcar la diferencia entre destacarnos o pasar desapercibidos. La comunicación eficaz es el pilar sobre el cual se construye una marca personal fuerte y reconocible. Esta no solo refleja nuestra identidad y valores, sino que también establece la percepción de confiabilidad y competencia ante los demás.

Un bio coworking eco-school en la educación superior dentro del marco de la educación disruptiva y la inteligencia artificial podría ofrecer un enfoque innovador y holístico para abordar los desafíos ambientales y promover un futuro más sostenible y equitativo para todos.

Ejemplos prácticos para cada punto y algunas referencias de autores relevantes en el campo de la permacultura, la sostenibilidad y la educación disruptiva, así como algunas herramientas de inteligencia artificial que podrían ser útiles:

La tecnología educativa, también conocida como EdTech, ha experimentado un crecimiento significativo en los últimos años, transformando la forma en que se enseña y se aprende. Estas empresas se dedican a desarrollar y aplicar soluciones tecnológicas en el ámbito educativo, con el objetivo de mejorar la calidad de la educación y hacerla más accesible para todos. A través del uso de herramientas digitales y plataformas en línea, la EdTech ha logrado reducir la brecha educativa y brindar oportunidades de aprendizaje a personas desfavorecidas.

La irrupción de la inteligencia artificial (IA) en el ámbito educativo ha desatado un gran debate en torno a su impacto en la evaluación del aprendizaje. Ante la preocupación de que la IA pueda facilitar la copia y el plagio por parte de los estudiantes, es crucial abordar esta problemática desde una perspectiva más profunda y transformadora. En lugar de culpar a la tecnología, es necesario repensar el enfoque pedagógico y los métodos de evaluación para adaptarlos a los desafíos y oportunidades que presenta la era digital.

Neurotechnology is a fast-expanding field dedicated to understanding the brain and creating technologies that interact with it.

From bioelectronic medicine that improves the quality of life to brain imaging that revolutionizes our conception of human consciousness, this technology has helped us to address many challenges. 

In the medical realm, where neurotechnology has been well regulated, it has led to significant progress in medical treatments. It has proved to have great potential to improve the lives and well-being of people affected by paralysis, neurological disorders, and mental illnesses. It can also treat depression effectively.

La Taxonomía de Bloom establece una serie de objetivos de aprendizaje jerarquizados de menor a mayor complejidad cognitiva. ¿La finalidad? Facilitar el diseño de los ejercicios y actividades adecuados para cada etapa, además de garantizar un aprendizaje que perdure toda la vida. Aunque desde su creación en 1956 ha pasado por diferentes revisiones para adaptarse a las necesidades educativas (y digitales) de cada época, la irrupción de la IA y de herramientas como ChatGPT o Gemini plantea nuevas posibilidades a los docentes para aplicar este recurso. Explicamos a continuación seis ejemplos de cómo el uso de la inteligencia artificial puede ayudar tanto a evaluar como a crear experiencias educativas que permitan lograr las habilidades de aprendizaje definidas en los diferentes niveles de la Taxonomía de Bloom. 

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: The AI Assessment Scale (AIAS).

The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognizing that there are instances where such tools may not be pedagogically appropriate or necessary.

By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in Education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the currentfocus on GenAI as a facilitator of academic misconduct.

The EdTech firm Anthology released a six-page AI policy framework  that aims to support higher education institutions that are interested in developing their own policies around the ethical use of AI. The framework was created to align with the AI Risk Management Framework from the National Institute of Standards and Technology, the European Union AI Act, and principles from the Organization for Economic Cooperation and Development. 

“It’s a responsibility for Anthology to bring this to Higher Education,” Bruce Dahlgren, Anthology’s chief executive, said in an interview. “In other words, we needed to be out ahead of this, we didn’t want to have our head in the sand. We made a conscious decision as a company to be the thought leader in AI for Higher Ed.”

It is the Learning TECHNOLOGIES Conference. We are in the most important technology transition since the invention of writing, an existential technology that is literally changing what we are as a species, therefore what we learn, why we learn and how we learn. AI was headlining at our Glastonbury. But as Ben Betts said, and I agree, it was all content production and add ons. The real AI in learning was like the Sex Pistols battering it out down the Thames, completely ignoring the Establishment in the Exel. It is so disruptive a force that no one knows how to deal with it, so they try to package it, contain it, get it to create courses, use it as a signal – look we’re down with this new tech! But no one is buying it - metaphorically or literally. It is bypassing L&D.

This paper, which was peer-reviewed and presented in 2023 at the 24th International Conference on Artificial Intelligence in Higher Ed, evaluated the performance of five types of AG questions to learn more about the way students interact with types of questions on tests and homework. The five types of questions, fill-in-the-blank, matching, multiple choice, free response, and self-graded submit and compare, were incorporated into digital textbooks using VitalSource’s free, artificial intelligence learning tool called Bookshelf CoachMe®.

The primary concern is that traditional course experiences with a significant online component reduce incentives, obligations, and opportunities for in-person engagement. This, in turn, potentially harms the learning experience, mental health, and arguably diminishes what is traditionally seen as a crucial formative life experience, though this last point is not explicitly stated.

Es importante reconocer que la IA puede ser un apoyo en la realización de tareas humanas, ya que permite recolectar y almacenar datos para luego clasificarlos, organizarlos y generar una respuesta. No obstante, estos avances también generan preocupación por sus posibles efectos negativos, como la discriminación, la desigualdad, la pérdida de privacidad, la manipulación, la censura y la falta de transparencia. Estos riesgos se deben a que la IA utiliza datos que pueden contener sesgos, ser incompletos o incluso erróneos. Por esta razón, no podemos idealizar la IA, sino que debemos conocer los desafíos e implicaciones que esta plantea en el ámbito educativo y analizar las dimensiones éticas de su funcionamiento y diseño.

Universities’ focus on assessment misconduct in the wake of the emergence of large language models “panicked” students, and institutions would have been better being “honest” that they were still figuring out the ramifications of new technologies, according to experts.