Summary
University educators use AI for teaching, research, and administration, especially for curriculum development and custom tools.
They automate tedious work but keep human oversight for creative or sensitive tasks like grading and advising.
Faculty are rethinking assignments and pedagogy as AI grows, though the report reflects early adopters on one platform.
Highlights
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In a recent Gallup survey, teachers reported that AI tools saved them an average of 5.9 hours per week.
La IA puede ayudar a liberar tiempo no lectivo vía un aumento de la eficiencia en labores administrativas o de preparación de clases.
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**Educators aren’t just using chatbots; they’re building their own custom tools with AI **Faculty are using Claude Artifacts to create interactive educational materials, such as chemistry simulations, automated grading rubrics, and data visualization dashboards.
Esta es una genial forma en la que los docentes pueden apalancar el uso de la IA para generar simulaciones que permitan a los estudiantes interactuar con sus objetos de aprendizaje.
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Educators tend to automate the drudgery while staying in the loop for everything else Tasks requiring significant context, creativity, or direct student interaction—like designing lessons, advising students, and writing grant proposals—are where educators are more likely to use AI as an enhancement. In contrast, routine administrative work such as financial management and record-keeping are more automation-heavy.
Esto tiene que ver con uno de los componentes del 4D Framework, creo que “Delegation”: ser capaz de discernir dónde puedo relajarme y dónde debo supervisar con cuidado el output de la IA.
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Using our privacy-preserving tool, we analyzed conversations from Claude.ai Free and Pro accounts associated with higher education email addresses and then automatically filtered conversations for educator-specific tasks—such as creating syllabi, grading assignments, or developing course materials.2 This filtering yielded approximately 74,000 conversations from a period in May and June. Our analysis should be viewed as an exploration of how educators use AI for profession-specific tasks, not a comprehensive view of all educator AI usage.
Lo insteresante de esta herramienta es que no les preguntan cómo la usan, sino que analizan directamente su uso, eliminando el sesgo de autorreporte.
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The most prominent use of AI, as revealed by both our Claude.ai analysis and our qualitative research with Northeastern, was for curriculum development. Our Claude.ai analysis also surfaced academic research and assessing student performance as the second and third most common uses.
El caso de uso más común de la IA entre los docentes el desarrollo curricular (planificaciones y desarrollo de actividades).
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Our qualitative research with Northeastern faculty hints at why educators often gravitate towards these common AI uses:
- Automation of a tedious task (“It takes care of the tedious tasks”; helps with “rote portions of fundraising”);
- Collaborative thought partner (“AI can find effective ways to explain concepts to students that I had not thought of myself”);
- Personalized learning experiences for students (“AI is useful for giving students and me individualized, interactive learning experiences beyond what one instructor could provide”).
Razones por las cuales los docentes utilizan IA para el desarrollo curricular
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“It’s the conversation with the LLM that’s valuable, not the first response. This is also what I try to teach students. Use it as a thought partner, not a thought substitute.”
Una de las capacidades intermedias de la interacción con modelos de lenguaje, utilizarlos como andamiaje para la reflexión.
New highlights added May 14, 2026 at 12:56 PM
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48.9% of grading-related conversations being identified as automation-heavy remains concerning. Although surveyed professors thought this was the single task that AI was least effective at, it was seen in the Claude.ai data. And even if this represents only 7% of the Claude.ai conversations we studied, it emerged as the second most automation-heavy task. This includes sub-tasks like providing feedback on student assignments and grading their work using rubrics. While it’s not clear to what degree these AI-generated responses factor into the final grades and feedback, the interactions surfaced by our research do show some amount of delegation to Claude.
A pesar de que los docentes declaran que la evaluación es un proceso que no se debe delegar completamente al modelo, la investigación de Anthropic muestra que es el segundo use case más automatizado. Interesante hallazgo que podría servir para invitar a la reflexión de cuerpos docentes sobre la delegación como componente del 4D Framework, sobre centauros inversos y sobre el discernimiento en general del uso de la IA en sus prácticas.
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Many educators recognize that AI tools are changing the way students learn. That in turn puts pressure on educators to change the way they’re teaching. As one surveyed professor put it: “AI is forcing me to totally change how I teach. I am expending a lot of effort trying to figure out how to deal with the cognitive offloading issue.”
Cognitive offloading como concepto importante para pensar en este tema, relacionado con “deskilling”
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It’s also changing what professors are teaching. In coding, for example, according to one professor, “AI-based coding has completely revolutionized the analytics teaching/learning experience. Instead of debugging commas and semicolons, we can spend our time talking about the concepts around the application of analytics in business.”
Algo que todavía no estamos abordando, es el efecto que la inteligencia artificial tiene la enseñanza de la especialidad de programación en la educación media Técnico profesional en Chile, lo que actualmente enseñamos es completamente básico y y va a enfrentar a los estudiantes a un mercado laboral que no necesita esas capacidades, entonces es urgente crítico que incorporemos estas herramientas agénticas de programación mediante inteligencia artificial en la formación de estos estudiantes para que puedan entrar con las capacidades que el mercado laboral le va a exigir así como también aprender directamente a partir del la implementación en Bassi cosas que ellos les interesan en la línea de aprendizaje basado en proyectos 
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One path forward may be to uplevel assignments based on these newfound tools and expect students to tackle more complex, real-world challenges that remain difficult even with AI assistance. However, this is a moving target given AI’s continual improvements and may put a significant burden on the educators themselves. Additionally, students still need to develop foundational skills independently of AI to effectively evaluate its outputs.
Esta es una objeción importante respecto de mi idea de cómo los andamiajes personalizados on demand permiten abordar desafíos reales complejos y “aprender haciendo”, en la línea del aprendizaje servicio. Los estudiantes aún deben desarrollar habilidades fundamentales, y esta vía puede reducir las oportunidades para que esto suceda. Hay que pensar ese equilibrio.