Launch HN:Bloomy (YC S26) – 面向 K-12 的 AI 驱动的精通式学习

17 分•作者: alexsouthmayd•3 个月前
大家好,我是 Alex Southmayd,Bloomy (<a href="https:&#x2F;&#x2F;bloomylearning.com">https:&#x2F;&#x2F;bloomylearning.com</a>) 的创始人。Bloomy 是一个为 K-12 学生提供人工智能驱动的掌握式学习平台。Bloomy 为学生提供人工智能导师和自适应课程(目前涵盖数学、英语语言艺术和写作)。 工作原理:我们诊断学生的技能差距,为他们制定个性化的学习路径,并提供符合标准的课程和苏格拉底式的人工智能导师,在不直接给出答案的情况下引导他们学习。 目标是利用人工智能解决 Bloom 的 2 西格玛问题(<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Bloom%27s_2_sigma_problem" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Bloom%27s_2_sigma_problem</a>)。 简短的发布视频:<a href="https:&#x2F;&#x2F;tinyurl.com&#x2F;bloomylearning" rel="nofollow">https:&#x2F;&#x2F;tinyurl.com&#x2F;bloomylearning</a> 更长的产品演示:<a href="https:&#x2F;&#x2F;youtu.be&#x2F;XHvoKt6qMeo" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;XHvoKt6qMeo</a> 家庭访问 Bloomy:<a href="https:&#x2F;&#x2F;bloomylearning.com&#x2F;families">https:&#x2F;&#x2F;bloomylearning.com&#x2F;families</a> 我曾是一名教师。我通过“为美国而教”(Teach For America)教授七年级英语和写作,每天都努力为 30 名有不同需求的学生提供差异化教学。有些学生需要补习,有些需要加速,还有很多学生需要一位导师坐在他们旁边,帮助他们思考下一步。 本杰明·布鲁姆的“二西格玛”理论——一对一辅导比传统课堂教学能产生更好的结果——对我来说一直很直观。难点在于让每个孩子都能负担得起并获得这种关注。 然后,人工智能改变了成本曲线。当我看到 Alpha 等学校围绕掌握程度而非在校时间来组织学术时,这个模式就清晰了。如果你听说过 Alpha School,我们受到的启发就是那种学习模式。但我一直在考虑已经存在的家庭和学校:在家上学的家庭、微型学校、混合式学校,以及大多数孩子目前所在的普通教室。 大多数学生和老师对学习差距的认识不够精确。他们得到的是一个分数、百分位数、基准分数或笼统的标准,而不是“这是这个学生接下来应该学习的技能”。现有的个性化学习产品常常感觉像数字练习册:它们提供了大量的练习,但诊断或教学却很少。很少有产品拥有人工智能导师来提供核心教学。 Bloomy 从诊断开始——我们与第三方评估集成并提供自己的评估——为每个学生创建学习路径。学生一次学习一项技能,接受简短的课程,以自适应难度进行练习,并在至少达到 90% 的掌握程度后才能继续前进。学习路径会根据学生的表现和我们关于技能先决条件的知识图谱(与 Learning Commons / Chan Zuckerberg Initiative 合作构建)进行更新。 每项技能有三个阶段。基础营(Base Camp)通过示例讲解概念。攀登(Climb)提供引导式练习和苏格拉底式支持。顶峰(Summit)是一项独立的、包含十道题的掌握评估,没有提示或人工智能辅助。学生需要达到 90% 的顶峰分数才能晋级。如果他们遇到太多困难,将被引导到更适合他们水平的不同技能。 BloomyBot 不是一个空白的聊天窗口,而是一个实时、互动且善于观察的数字导师。在练习过程中,它会接收当前的段落或问题、问题本身、学生的尝试、作者的解释以及相关的误解背景。它遵循一个分层辅导阶梯:首先询问学生尝试了什么,然后指出概念,建议策略,一起完成一个步骤,只有在学生遇到困难后才提供更强的支持,并在此过程中适应和学习学生的情况。学生可以打断它,我们已经开始为客户要求的西班牙语、法语以及其他一些更小众的语言提供多语言支持。 我们目前为 BloomyBot 使用各种 Anthropic 和 OpenAI 模型。该导师仅限于当前课程,会重定向不相关的提问,限制对话长度,并在掌握评估期间不可用。语言模型不选择课程,也不决定学生是否掌握了某项技能。 这种分离很重要。一个通常被认为是“有帮助”的人工智能回复,可能是一个糟糕的辅导回复:如果它直接给出答案,学生完成了任务,但可能什么都没学到。我们的目标不是构建一个回答家庭作业的聊天机器人。而是将人工智能置于一个结构化的诊断、教学、练习、反馈和独立掌握的循环中。 大型语言模型仍然可能出错,我们并不声称我们的限制消除了这种可能性。我们通过将 BloomyBot 建立在作者编写的课程内容上,保持其主题相关性,记录对话,并将其排除在评估之外来减少出错的几率。教师和家长可以审查辅导活动,学生可以报告问题,安全信号会触发人工警报和备份审计。 我们也不认为 Bloomy 会取代教师、家长或人类导师。一个优秀的人类导师更好。我们正在测试的更具体的问题是,在一个学生本就会进行的、有界限的学习会话中,一个上下文感知的导师是否能提供比静态的“正确/错误”反馈更好的帮助。从长远来看,问题将更多地在于,学生在一对一的人工智能辅导(至少在课程的某些方面)下,是否会比在中等或大型教室的多对一教学下表现更好。 Bloomy 目前已在多种环境中得到应用:传统学区、特许学校、混合式学校、微型学校、在家上学的家庭以及寻求额外学术支持的家庭。在马萨诸塞州一所为约 150 名 6 至 8 年级学生服务的特许学校进行的早期试点中,学生的 NWEA MAP 增长平均约为冬季到春季预期增长的 1.8 倍。这是一个观察性试点,而非随机研究,因此我们将其视为一个令人鼓舞的信号,而不是 Bloomy 导致差异的证明。 家长和老师可以看到学生掌握了什么、正在学习什么以及可能需要支持的地方。我们发现,成年人通常不想要另一个通用的分数;他们想知道本周有哪些少数技能值得关注。 Bloomy 的收入来自家庭订阅和学校许可。ELA 每月收费 39 美元,每年收费 279 美元(每位学习者),Writing Studio 每月收费 19 美元,每年收费 139 美元。数学课程计划于 7 月 31 日推出,价格与 ELA 相同。学校和微型学校按学生收费,价格因学科覆盖范围、入学人数、学生名单和实施需求而异。 由于儿童使用 Bloomy,我们会收集学习响应、进度数据和辅导对话。我们不会出售个人信息,不会使用儿童数据进行行为广告,也不会允许模型提供商使用 Bloomy 发送的可识别儿童数据来训练通用模型。我们与 Anthropic 和 OpenAI 都签订了零数据保留协议。家长和学校可以根据适用的账户或学校协议请求访问、导出、更正或删除数据。 关于我的更多背景:在加入“为美国而教”之后,我为 Manhattan Prep / Kaplan 设计了 GMAT 和 GRE 课程,并教授相关课程;我曾领导 Lyft 在新英格兰地区的司机招募团队;我在斯坦福完成了 MBA 学位;并在麦肯锡领导人工智能转型项目(因此,当模型在今年一月终于足够好,能够实现我正在用 Bloomy 追求的目标时,我正处于一个恰当的时机开始构建)。Bloomy 汇集了我职业生涯中最关心的不同部分:教育成果、学习设计、产品构建以及将有用的技术交付到人们手中。 我特别希望得到家长、教师、从事儿童面向人工智能工作的工程师,以及那些构建过辅导、评估或自适应学习系统的开发者的反馈。引导式人工智能帮助与独立掌握之间的分离是否合理?您认为人工智能在教育领域最大的潜力在哪里?我们的安全措施在哪些方面不足?您需要什么样的证据或产品行为才能信任这样的系统会用于学生? 当然,我们必须警惕许多危险和陷阱,但我相信,如果我们负责任且明智地使用人工智能,我们真的可以在(长久以来首次)推动 K-12 教育取得显著进步。
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Hi HN, I’m Alex Southmayd, the founder of Bloomy (<a href="https:&#x2F;&#x2F;bloomylearning.com">https:&#x2F;&#x2F;bloomylearning.com</a>) – 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).<p>How it works: we diagnose students’ 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.<p>The goal is to solve the Bloom 2-sigma problem (<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Bloom%27s_2_sigma_problem" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Bloom%27s_2_sigma_problem</a>) with AI.<p>Short launch video: <a href="https:&#x2F;&#x2F;tinyurl.com&#x2F;bloomylearning" rel="nofollow">https:&#x2F;&#x2F;tinyurl.com&#x2F;bloomylearning</a><p>Longer product demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;XHvoKt6qMeo" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;XHvoKt6qMeo</a><p>Families access for Bloomy: <a href="https:&#x2F;&#x2F;bloomylearning.com&#x2F;families">https:&#x2F;&#x2F;bloomylearning.com&#x2F;families</a><p>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 Bloom’s two-sigma result—that one-on-one tutoring can produce much better outcomes than conventional classroom instruction—always felt intuitively true to me. The hard part was making that kind of attention affordable and available to every child.<p>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 you’ve 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.<p>Most students and teachers see learning gaps at the wrong resolution. They get a grade, percentile, benchmark score, or broad standard—not “this is the next skill this student should learn.” 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 diagnostic—we integrate with third-party assessments and provide our own—and 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 &#x2F; Chan Zuckerberg Initiative).<p>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, they’ll be routed to a different skill better suited for their level.<p>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 student’s 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 we’ve begun to roll out multilingual support for Spanish, French, and a few other more niche languages that customers have asked for.<p>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.<p>That separation is important. A conventionally “helpful” 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.<p>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 “correct&#x2F;incorrect” 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.<p>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.<p>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.<p>Bloomy makes money through family subscriptions and school licensing. ELA costs $39&#x2F;month or $279&#x2F;year per learner, and Writing Studio costs $19&#x2F;month or $139&#x2F;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.<p>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.<p>More background on me: after Teach For America, I taught and designed GMAT and GRE curriculum for Manhattan Prep &#x2F; Kaplan, led the driver acquisition team for Lyft’s 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 people’s hands.<p>I’d 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?<p>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.