- I think the other mistake I see here is trying to over-engineer a deterministic learner path instead of giving the AI more free reign on best next interaction and a set of goals it needs to accomplish through the session; it can feel more responsive and free-form that way from the learner's POV.
- If you had voice here you could also make the screen optional. In my experience typing out long answers to questions can take a while too - so a voice mode might be helpful for learners. It would also be cool if people could take a 'photo of their work' for e.g. math equations done by hand.
- To the earlier point on family end-market, an interesting idea is modeling bloomy - have some grown-up oriented courses so you can learn with / side-by-side with your child? Just an idea.
I am here to tell you HELL YES and TAKE MY MONEY. I am such a fan of the idea of using AI to help give personalized and structured AI lessons in the hands of students and let them cook!
- It looks like you are gating family access to K-3 for now and I think that's right. I wouldn't really be comfortable giving my first-grader a live chatbot. Maybe I would think about whether there are other non-persona modalities that could still be self-directed (i.e. I am uncomfortable with a chatbot interface on this for a six year old but gamified flash cards with options could be different).
- I think the other issue is with motivation. I have various duct-tape versions of these types of agents and the thing about it is if you're doing the learning right it can be HARD. So I would think about using motivational interviewing or other techniques to help keep the user coming back and motivated.
- I would really think about the assessments here too. Many people are worried about LLMs ruining student evaluations, but if you could bake in reliable, flexible exams that gauge user progress (even for something like a "Did you read this" quiz) I would bet teachers would like it. There is likely so much you could do on student progress observability and e.g. structuring team-based projects or having targeted student working groups to hash out hard concepts in a targeted way, etc.
This is such an interesting market and use case too because the educational system might be very structurally set up to the current pedagogical staffing model (think about the incentives for teacher's unions and administrators). If you think it will be hard to change that system as quickly as you want I would also try to have an offering direct to families / home-schoolers. I think there is also a cottage industry of tutors that might benefit. Maybe partnering with the textbook publishers? I'm sure there are some "Teach your kids better" influencers that would get you into some feeds?
IME LLMs are kind of like a projection of your current expertise - your prompting and guidance etc. biases LLM plans kind of 'in the direction' of your thinking. I think this is one reason why it seems like senior engineers get more lift vs. juniors.
What I am exploring is another step to the classic 'research / plan / implement' pattern: 'research / plan / LEARN / implement' where LEARN involves the human doing AI tutoring sessions to ensure a deep understanding the concepts etc. that the LLM is planning to implement so you can refine / iterate on plans and direct the LLM in ever more effective ways. My idea is that this then compounds your human capital and reduces the occurance of 'sounds smart, doesn't work' pattern.
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Commoditize your complement - I expect to see this most in consumer AI (after that starts actually working...)
It will be important for Apple to have good enough, cheap local LLM models that run on-device.
If the barrier to performance shifts from fundamental model capability to context collection and management I would expect to see folks focused on that problem continuing to drive open-weight LLM model development in some shape or form.
My take is that B2C AI applications are kind of structurally limited by how hard it is to build personalized context.
The idea of capable local models could be a huge unlock here if they are able to do the bottom-up context collection research / tagging / etc. at scale.
I think the research / plan / execute idea is good but feels like you would be outsourcing your thinking. Gotta review the plan and spend your own thinking tokens!
Yeah I vibe coded a simple app that takes an org-mode file, renders it as a kanban board, and lets me spin up agents for each task with the prompt in the body in a named tmux session. The frontend gets updated via Claude code hooks when an agent is idle.
I think the key is to combine human and agent task tracking in one pane of glass.
I have been doing the same but with happy. It works quite well for quick brainstorms etc. but for deeper work on a real research / plan / implement thing I think you need to actually engage with the output which is hard to do on mobile. Maybe if I had a better UI than terminus to read and check the remote files I would be able to get more done.
I am also hoping / trying to put Claude code on top of a personal zettlekasten to automate more of my “personal life” tasks and get more stuff done for me. Haven’t gotten it really singing yet but I think that could also be really cool.
This isn't study mode, it's a different AI tutor, but:
"The median learning gains for students, relative to the pre-test baseline (M = 2.75, N = 316), in the AI-tutored group were over double those for students in the in-class active learning group."
Looks really cool - I noticed Enterprise has smart consent management?
The thing I think some enterprise customers are worried about in this space is that in many jurisdictions you legally need to disclose recording - having a bot join the call can do that disclosure - but users hate the bot and it takes up too much visibility on many of these calls.
Would love to learn more about your approach there