These look like system and agent prompts and agent instructions: "Academic Assistant Pro.md", "Code Tutor" https://github.com/linexjlin/GPTs/blob/main/prompts/
Gemini Notebook does flashcards, study guides: https://edu.google.com/ai-gemini-notebook/
Spaced Repetition flashcards or flashquestions to progress through a game might be good; configure Anki decks to surface cards from when for example the user jumps to crush a bonus block.
"Ask HN: How can I learn to read mathematical notation?" (2018) https://news.ycombinator.com/item?id=18511718
"[Feature Request]: flashcards to advance in the game and learning mode" Issue #3054 ; SuperTux/supertux https://github.com/SuperTux/supertux/issues/3054
So I Ctrl-F'd for "Linear algebra" resources in my research log and found:
"Interactive Linear Algebra (2019)" (2021) re: Manim https://news.ycombinator.com/item?id=28173565
Latest Manim developments; interactive WebGL in browser: https://news.ycombinator.com/item?id=49093210
The AI sloppification is palpable.
Anywho, curious to see what comes out of this! EdTech & AI is a really promising space.
Sadly we do not, so something like this will have to serve as a more accessible backup.
Perhaps I am a bit more optimistic, but if AI could give me, let's say, 90% of an average lecturer's capability, at 10% of the price or even less, that is probably extremely valuable. (For one, I would certainly be interested in learning new things that otherwise wouldn't make sense financially)
But I did not learn everything I wanted to know. And I can’t afford to take a time-out to go to grad school again for a second PhD. On the other hand, an LLM, in some sense, has access to the world’s knowledge. With some patience and persistence, you can mine almost-expert-level instruction from one.
When ChatGPT 3.5 came out, I excitedly tried this for a topic that I WAS an expert in. The results were discouraging. The model’s answers seemed to reflect the general misunderstandings that people had about the topic. More recently though, I had to review a paper in the same area, and I used a late-model ChatGPT to help check my work. It was an eye-opening experience because it was no longer confused. And it found longstanding misunderstandings I had, buttressing its answers against my skepticism with citations to original work. I came away very impressed. This kind of AI “rubber duck programming” is my preferred style of use now. I used it just today to help me learn an area of statistics I have always been fuzzy about. This approach definitely requires some careful prompting, but I am optimistic that AI tutors will one day be a real thing. My only worry is that people lose the ability to understand what makes an answer a good one and why we should care about good answers.
Yeah, prominent thing run by famous guy … but why so much funding so early?
What does $100M enable in the next 5-10 years that $25M doesn’t?
Given how much people spend on education, there’s no reason AI education software wont be a huge market.
And there’s few people better suited than Andrew Ng to execute this
At that point they had ~24 employees. He said his interest was crowdsourcing and thought most of the engineering effort would go toward the crowdsourced translation, but at that time, it was being handled by just two employees.
They brought on two prominent consultant researchers, leaders in the field of language learning and linguistics. The first question they asked the consultants was which part of speech should they present to the learner first. The consultants didn't have an answer. They decided to dedicate engineers to collecting data in order to answer that question.
A big problem with edTech is that we don't know.
So people will replace AI?
Hmmm. Maybe they should rethink their motto.
I need to get some model pricing evals in place to see if I can use open weight models to get it cheap enough, since it's just me and not VC money. The real product potential seems to be getting reusable materials like cert prep, continuing education for careers that require it, and the like. But then you're fighting incumbents that fiercely control their moats.
The concern, though, is that how to achieve those 3 thesis. Do they train/fine-turn their own model? according to my own experience, the current models are not well trained in personal education context, and it is either too textbook-like or too task-driven.
I don't really get how this is new or even "AI". Can't you already ask Claude to teach you something ?
"Patiently stays with you until you've mastered new skills", that's so nice. Maybe in the future it will be the other way around, your computer will complain about how slow the user is :)
My wife has been a high school math teacher for 25 years and has seen waves of tech luminaries give their visions of how tech will radically improve education and learning. She can’t point to any startup that has had a major impact in improving outcomes. I wish I could point her to something that really will have an impact in, or even out, of the classroom for her students.
This is of course about the PhD level and beyond. For regular university-level education, where the goal is to pass the usual exams, ChatGPT and Claude are more than enough. But for real-world "doing", not (yet).
We know that mild stress can narrow focus and help learning. "Neural connections do form when you struggle." [0] A little stress is not only good but necessary for learning while a lot of stress causes a breakdown. The trick is being able to judge the stress level of the student and every person is different.
The idea of using some type of bio metric data from EEG or blood pressure sensors will help determine a little struggle from a lot of struggle in formative assessment.
The best teachers seem to combine deep thinking about how to teach with intuition and charisma. The ingredients are hard to replicate.
I do think that this is a nut that we will eventually crack, but it is proving to be a very tough nut.