DeepMind chose tutoring as a test case for Gemini because it demands so many subtasks at once: lesson planning, asking good questions, giving feedback, checking work, and building metacognition. Mastering tutoring teaches the model skills that transfer across education.
It's so interesting. So stay with it then like as you've been putting it in this very specific use case right around the tutor. I curious like why did you choose tutoring given that it is so different from the other LLMs? Right. Sort of the that that assistant purpose and how are you constraining it to make sure that it's the most useful tutor right that, that, that. That a student could have access to as opposed to maybe its natural instincts based on its foundation.
So we chose the tutoring use case because the way we call it it's kind of learning or education complete. So what that means is that in order to tutor well, you kind of need to know all the different type subtasks or capabilities that are important for education. So you need to be able to plan a lesson, you need to be able to ask good questions and provide good feedback and check the students work, among many other kind of things about metacognition and active engagement. So if we really manage to figure out the tutoring use case, then the resulting kind of underlying model Gemini can then be used for all these other tasks. A great way to bring one single goal to optimize for what can then result in broader benefits for learning.
