Many people use AI to boost learning efficiency — for example, by summarizing dense material into concise bullet points or translating foreign texts into their native language — essentially allowing them to do what they were already doing, only faster. But I noticed that these efficiency gains, when accumulated, reveal a deeper transformation: learning methods that once felt too time-consuming to attempt have suddenly become feasible. As a result, I think the most important question to ask is not “how to learn more efficiently,” but “how to become capable of learning knowledge that is more complex, abstract, and challenging.” (View Highlight)
I believe the value of learning does not lie in accumulating as much knowledge as possible, but in cultivating the ability to think more deeply about important questions. And often, those most important questions are also the ones that demand the hardest and most challenging knowledge. (View Highlight)
The true nature of a whiteboard is that it’s a giant, borderless desk. Whether you consider yourself a visual thinker or not, everyone needs a desk. The size of your desk determines how much information you can see at once, which in turn influences how far your thinking can reach. Of course, smart people often have a large “virtual desk” in their brains — their working memory, the amount of information they can hold at a given time. But whenever you’re dealing with problems that exceed that mental capacity, a giant desk, a big screen, or an infinite whiteboard that can hold large amounts of information becomes one of the most powerful tools available. It frees you from the tiny rectangle of a physical book or e-reader. (View Highlight)
Sobre la metáfora de la pizarra para la memoria de trabajo y el uso de pizarras reales para extender esa capacidad cognitiva.
At the end of my reading session, while everything is still fresh in my mind, I always take notes. My note-taking style is fairly simple: I start with a blank note and try to rewrite everything I’ve learned in my own words, as if I were teaching it to someone else. This process helps me spot and fill in logical gaps I hadn’t noticed while reading. It’s similar to what some of my professor friends do when they want to learn a new field — they do it by teaching a course. (View Highlight)
Enseñar como una estrategia para aprender.
when my notes grow really long, I spend time breaking them down into smaller parts and visualizing their relationships. I find this process extremely useful for developing a deeper understanding of what I’ve learned. This is also the stage where drawing arrows and connections becomes really helpful. (View Highlight)
Diagramas y mapas conceptuales como estrategia para el aprendizaje.
Many argue that using AI too much is “outsourcing thinking,” making people worse at it. My experience is the complete opposite. For me, AI enables me to engage with harder materials and spend more time thinking. It’s the best teacher and thinking partner I could ever ask for. And believe me or not — I have very high standards for what makes a great teacher. (View Highlight)
Sobre la tensión asociada al efecto de la IA sobre el aprendizaje: promueve o inhibe? Depende del uso que se le da: entrenador o masajista? Lograr lo primero requiere de entrenamiento. Se puede relacionar con concepto del “uso” del terapeuta en Winnicott.
Learning is one of those domains where “outsourcing” is meaningless. Having AI solves 100 math problems for you won’t make you better at math. But having AI gives you hints when you’re stuck on those 100 problems can keep you persistent and motivated. In the end, you decide how to use the tool. (View Highlight)