Launch HN: Bloomy (YC S26) â AI-powered mastery learning for K-12
78 points - yesterday at 4:32 PM
Hi HN, Iâm Alex Southmayd, the founder of Bloomy (https://bloomylearning.com) â an AI-powered mastery-learning platform for K-12 students. Bloomy provides students with an AI tutor alongside adaptive curriculum (right now Math, English Language Arts, and Writing).
How it works: we diagnose studentsâ skill gaps, place them on personalized learning paths, and give them standards-aligned lessons and a Socratic AI tutor that scaffolds their learning without just giving away the answer.
The goal is to solve the Bloom 2-sigma problem (https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem) with AI.
Short launch video: https://tinyurl.com/bloomylearning
Longer product demo: https://youtu.be/XHvoKt6qMeo
Families access for Bloomy: https://bloomylearning.com/families
I started as a teacher. I taught 7th-grade English and writing with Teach For America, and every day I struggled to deliver differentiated instruction to 30 students with 30 different sets of needs. Some students needed remediation, some needed acceleration, and many needed a tutor sitting next to them helping them reason through the next step. Benjamin Bloomâs two-sigma resultâthat one-on-one tutoring can produce much better outcomes than conventional classroom instructionâalways felt intuitively true to me. The hard part was making that kind of attention affordable and available to every child.
Then AI changed the cost curve. When I saw schools such as Alpha organize academics around mastery rather than seat time, the model clicked. If youâve heard of Alpha School, that is directionally the kind of learning model that inspired us. But I kept thinking about the families and schools that already exist: homeschool families, microschools, hybrid schools, and regular classrooms where most children are today.
Most students and teachers see learning gaps at the wrong resolution. They get a grade, percentile, benchmark score, or broad standardânot âthis is the next skill this student should learn.â Existing personalized-learning products often feel like digital worksheets: they provide plenty of practice, but not much diagnosis or teaching. Very few have AI tutors providing the core instruction. Bloomy starts with a diagnosticâwe integrate with third-party assessments and provide our ownâand creates a learning path for each student. Students work one skill at a time, receive a short lesson, practice at an adaptive difficulty, and only move forward after demonstrating at least 90% mastery. The learning path updates as the student works, based on their performance and our knowledge graph of skill prerequisites (built in collaboration with Learning Commons / Chan Zuckerberg Initiative).
Each skill has three stages. Base Camp teaches the concept with worked examples. Climb provides guided practice and Socratic support. Summit is an independent ten-question mastery assessment with no hints or AI assistance. Students need to achieve 90% on the Summit to advance. If they struggle too much, theyâll be routed to a different skill better suited for their level.
BloomyBot is not a blank chat window but rather a live, interactive, and observant digital tutor. During practice, it receives the active passage or problem, the question, the studentâs attempt, an authored explanation, and relevant misconception context. It follows a scaffolded tutoring ladder: first asking what the student tried, then pointing toward the concept, suggesting a strategy, working through one step together, and only providing heavier scaffolding after the student has struggled, adapting to and learning from the student along the way. Students can interrupt it, and weâve begun to roll out multilingual support for Spanish, French, and a few other more niche languages that customers have asked for.
We currently use a variety of Anthropic and OpenAI models for BloomyBot. The tutor is restricted to the current lesson, redirects unrelated questions, limits conversation length, and is unavailable during mastery assessments. The language model does not choose the curriculum or decide whether a student has mastered a skill.
That separation is important. A conventionally âhelpfulâ AI response can be a bad tutoring response: if it gives away the answer, the student completes the task but may not learn anything. Our goal is not to build a homework-answering chatbot. It is to put AI inside a structured loop of diagnosis, instruction, practice, feedback, and independent mastery.
LLMs can still be wrong, and we do not claim our constraints eliminate that. We reduce the surface area by grounding BloomyBot in authored lesson content, keeping it on topic, logging conversations, and removing it from assessments. Teachers and parents can review tutoring activity, students can report problems, and safety signals trigger human alerts and a backup audit. We also do not see Bloomy as a replacement for teachers, parents, or human tutors. A good human tutor is better. The narrower question we are testing is whether, during a bounded learning session a student would already be doing, a context-aware tutor can provide better help than static âcorrect/incorrectâ feedback. Longer term, the question becomes more whether a student would perform better with one-on-one AI tutoring (at least in certain aspects of the curriculum) than with many-to-one instruction in a medium- or large-sized classroom.
Bloomy is now being used across several settings: traditional districts, charter schools, hybrid schools, microschools, homeschools, and families looking for additional academic support. In an early pilot at a charter school in Massachusetts serving ~150 students in grades 6 through 8, students averaged roughly 1.8 times the expected winter-to-spring NWEA MAP growth. This was an observational pilot, not a randomized study, so we treat it as an encouraging signal rather than proof that Bloomy caused the difference.
Parents and teachers can see what a student has mastered, what is in progress, and where support may be needed. We have found that adults generally do not want another generic score; they want to know which small number of skills deserve attention this week.
Bloomy makes money through family subscriptions and school licensing. ELA costs $39/month or $279/year per learner, and Writing Studio costs $19/month or $139/year. Math is scheduled to launch July 31 at the same price as ELA. Schools and microschools pay per student, with pricing varying by subject coverage, enrollment, rostering, and implementation needs.
Because children use Bloomy, we collect learning responses, progress data, and tutoring conversations. We do not sell personal information, use child data for behavioral advertising, or permit model providers to train general-purpose models on identifiable child data sent by Bloomy. We have Zero Data Retention agreements with both Anthropic and OpenAI. Parents and schools can request access, export, correction, or deletion under the applicable account or school agreement.
More background on me: after Teach For America, I taught and designed GMAT and GRE curriculum for Manhattan Prep / Kaplan, led the driver acquisition team for Lyftâs New England markets, completed an MBA at Stanford, and led AI transformation projects at McKinsey (so when models finally became good enough this past January to achieve the kinds of things I am pursuing with Bloomy, I was in the right place at the right time to begin building). Bloomy brings together the different parts of my career that I care most about: educational outcomes, learning design, building products, and getting useful technology into peopleâs hands.
Iâd especially value feedback from parents, teachers, engineers working on child-facing AI, and people who have built tutoring, assessment, or adaptive-learning systems. Does the separation between guided AI help and independent mastery make sense? Where do you see the greatest potential with AI in education? Where are our safeguards insufficient? What evidence or product behavior would you need to trust something like this with a student?
Certainly there are many dangers and pitfalls we must beware of, too, but I believe we can really move the needle in K-12 (for the first time in a long time) if we use AI responsibly and intelligently.
Comments
- 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?
I worked at an edtech startup with adaptive learning platforms in previous decades. One thing that is always difficult to surmount was that at the under K12 level were:
1. B2b it was hard to land large contracts(like getting an entire public school district to jump from large publishers like HMH or Wiley) because it turned out that the teachers themselves did not care about adaptivity when they had a thousand other things to worry about. But teachers are not the ones that make the deals. It's the school district board and The reps at large publishing just said we are also adaptive and no one would care about how you differentiated. Teachers and students really aren't the lot you have to convince in a b2b deal do you :)?
2. If you want the b2c route then the customer acquisition cost was way too high because again like to get teachers and students to actually adopt this to help them was really a hard sell, especially when the public school system has teachers that are so overburdened with other stuff.
I see that you mentioned charter schools for your pilot, which are not exactly public schools but I dunno of they come with the same set of challenges, but I wonder are these above two challenges are Also something you have had to face while scaling your product?
You've picked one hell of a time to enter the K12 market. 1:1 device backlash, AI tool scrutiny hightening, teacher exhaustion, no recovery in sight for the recent funding cliff.
Your pilot model is the way to go. My favorite tools all grew out of that model. Stay patient, listen to the early users, encourage them to spread the word. Dont' be above grinding it out at a table at some of the smaller regional educaitonal confreneces.
Sounds like you're already doing this but take IXL head on. They are ripe for the picking.
Good luck!
Couple comments on this being 'screen' oriented, which are as you say endemic in schools and terrible.
There's a fair amount of research that paper reading and handling and writing gets significantly higher retention than screen reading. Most especially physically writing notes, but generally comprehension is just higher for paper. Interestingly e-ink is in the middle between screens and paper.
VLMs offer the interesting possibility of 'looking over the shoulder' of students as they work examples and problems by hand through a camera. It's a harder lift, and it's more expensive, but it might find much higher buy-in to think about a combo book / workbook / AI tutor business model, not least because if you talk to almost any parent or teacher they will tell you that they are extremely worried about genz/alpha kids' abilities to focus, read with attention for longer periods of time or handwrite.
Many of the exercises can be evaluated through a VLM pretty much as easily as in a chat window -- more expensive inference, but possibly a much better world.
A middle ground here might be experimenting with something like a remarkable tablet - it's relatively easy to vibe code a responsive eink page right now, and you could do away with the camera side. I guess I'd think of pairing it with a phone app to talk that could hook up to the remarkable.
My pet theory on handwriting notes and why they're so much better than typing or just listening - if not in shorthand, they are too slow to capture lectures word for word, and thus force a first summarization/categorization step, meaning stuff has to literally go in to short term memory then get pulled out and processed; this just has to be better than typing in a sort of fast typing haze and trying to read them later. It's also the reason my college notebooks have things like (WTF?? <---) next to notes -- I was trying to synthesize and couldn't in the moment. In addition to all this, I understand brain scans show quite a high percentage of the brain gets involved when drawing/handwriting notes -- you have micro and macro muscle movements plus all the cognitive and visual tasks together.
At any rate, it will be a great service to learners to have a quality AI tutor, and I'd pitch you on looking seriously at e-ink or paper modalities if you want to help students learn even better and faster. The tech is SUUUPER close right now, or even could be there with a little willpower.
How do you solve for "meaning?" Is there some implicit expectation that kids arrive with motivation to discover meaning, or some extrinsic rewards?
As AI essentially commoditizes the mechanical aspects of learning, this is the question we find ourselves asking.
I would like to share our journey running a microschool (where our kids go, along with 40 others now) and how we use AI and what we think about its role.
Some useful context, because this really has informed our approach. I am an engineer turned neuroscientist and the co-author of a popular nonfiction book on the mind (and I share this here because the hard effortful self-driven work of crafting this book taught me more about the mind than anything else in my PhD or later). This work, done during the pandemic when learning was coming online, was what pushed us into setting up the school and gave us the courage to work on it from first principles and not rely on any off-the-shelf alternative pedagogy (montessori, waldorf etc). We looked at (and continue to work on) the full-stack of philosophy, pedagogy, process, practice. So the "what should they learn" is also met with "why should they learn this" and also "how should they be learning this"
The *single biggest* learning for us, which we are now able to articulate clearly, is that schools are set up to fail because there is a well-intentioned but misguided focus on measurement (what are they learning). This gravitational pull results in a focus on mechanics (the most easily measurable thing; long division, periodic tables). This results in a narrow focus on concepts (what David Perkins terms elementitis). Most importantly, what gets left out is meaning. Why should I care about this? With a well-intentioned curriculum and an administration pipette we basically leach out agency and meaning.
So we try to solve for meaning. Our biggest worry with AI is that it focuses entirely on mechanics, with very little meaning. Adults and kids are learning to discount AI-content as automated and inauthentic. We derive meaning from connection. AI can help with the mechanics, but only when meaning has been established. Without this clear-eyed understanding and buy-in we basically have chromebooks all over again, which flattened learning to 2D screens and accomplished nothing (if you look at literacy numeracy scores)
Chatbots are great if you start with meaning and motivation. I already care about the why, so I will socratically find my way to the how and what. But if we do not care about it, which really is the biggest malaise (why should a child who has not yet explored the world or literature they like care about foreshadowing or plot mountains?) Sure, the AI could make it easier, but the kids still need to care?
Wrote about our meaning > motivation > mechanics > measurement revelation and the usual unfortunate inversion here https://blog.comini.in/p/schooling-has-a-meaning-crisis-para...
We need many more people approaching education with ideas and experiments, so thank you and good luck!
does bloomybot ever just let a wrong approach play out and do the postmortem after? also when a struggling student gets rerouted to an easier skill, can you tell that apart in your data from one who was thirty seconds away from the useful kind of failure? the summit gate tells you they arrived. it doesn't tell you whether the help in the middle did the teaching.
If your metrics are good, increasing test scores is the thing thats 100% going yo get schools on board
How does this factor into your business?
There is certainly room for software to design a curriculum for the parent or teacher to use. We have tried (and failed) to find such a thing for our homeschool curriculum.
But giving the kid a screen? If you're even a midge honest with yourself, a screen will de-educate your child, leading to the opposite of the intended consequence.
Every quality of life metric (life expectancy, income, etc) goes up with better education. I guess itâs because most people in tech grew up in neighborhoods with nice schools and went to college so they donât think about it.
Anyway, great luck.
We are also approaching Bloomâs 2-sigma challenge as the design goal. The aim is not another chatbot that gives away answers, but a patient, Socratic coach operating within a structured system of diagnosis, guided practice, feedback, productive struggle, and independent mastery.
One reason we chose college O-Chem rather than Kâ12 is precisely the concern raised throughout this thread about screen time and introducing conversational AI too early. Organic Chemistry is a notoriously difficult gateway course taken by older students who are already studying digitally. It gives us a more bounded, age-appropriate environment in which to test whether AI can strengthen effortful learning rather than replace thinking.
Your separation of the tutor from the underlying curriculum and mastery system is especially important. We have reached a similar conclusion: AI can ask better questions, provide patient scaffolding, and adapt how concepts are explained, but it should not independently define the curriculum, determine mastery, or become the source of truth. We therefore keep curriculum, pedagogical policy, mastery decisions, and safety controls in a deterministic orchestration layer outside the model.
Congratulations on the launch. It is encouraging to see others pursuing the same underlying educational ambition from a different starting point.
[1] chemio.ai
I would need the product to not exist. I don't want this anywhere near a child. I want humans instructing humans, teaching empathy, connection, and - most importantly - learning to learn.
Anything short of that is just building Brave New World. Electroshock the kids if they reach for forbidden knowledge.
I'm sorry to be so negative since I'm sure you worked hard on this but I find all products in this category to be 100% reprehensible.
"Computer-assisted learning. What an insult, to have the computer teach the human."
- Russ Ackoff