VERTICAL · 02

Learning depends on AI
that never fails. 学习效果依赖
从不失效的 AI。

AI tutors that hallucinate cost learners real outcomes. ZERORE makes learning outcome alignment measurable — so every AI-powered lesson delivers what it promises. 产生幻觉的 AI 家教会让学习者付出真实代价。ZERORE 将学习成效对齐转化为可量化指标，让每一堂 AI 驱动的课程都能兑现承诺。

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The challenge行业挑战

Wrong answers have real consequences. 错误答案有真实后果。

In education, there's no room for confident wrongness. An AI tutor that hallucinations a formula, misstates a date, or misreads a learner's level doesn't just lose a user — it damages their progress. 在教育场景中，没有自信出错的余地。AI 家教若幻觉出错误公式、误述历史日期，或误判学习者水平，不仅会流失用户，更会损害其学习进度。

PROBLEM · 01

Hallucination at curriculum scale 课程规模下的幻觉问题

LLMs generate plausible-sounding wrong answers. In a high-stakes curriculum, this isn't a UX bug — it's an educational liability. Detecting hallucinations across millions of learner interactions requires infrastructure, not manual review. 大语言模型会生成听起来合理但实际错误的答案。在高风险课程中，这不是体验问题，而是教育责任。跨百万学习交互检测幻觉需要基础设施，而非人工审核。

PROBLEM · 02

Difficulty & level mismatch 难度与水平错配

Adaptive tutors that miscalibrate difficulty — too hard, too easy, wrong prerequisite assumptions — create invisible learning gaps. Learners disengage before you measure the drop. 对难度误校准的自适应家教——太难、太易或前置假设错误——会产生不可见的学习缺口。学习者在数据反映下滑之前就已脱离。

PROBLEM · 03

No outcome measurement 缺乏成效衡量

Most edtech teams track engagement — not learning. Sessions completed, time-on-task, click-through. None of these tell you whether the learner actually understood. You're optimizing the wrong metric. 大多数教育科技团队追踪的是参与度，而非学习效果。完成的课程数、任务时长、点击率——这些都无法告诉你学习者是否真正理解了。你在优化错误的指标。

Hallucination detection at scale 规模化幻觉检测

Flag factual errors, contradictions, and curriculum mismatches across every learner interaction — automatically, without sampling. Wrong answers don't get a second chance to mislead. 对每次学习交互自动标记事实错误、矛盾与课程失配——无需抽样。错误答案没有第二次误导学习者的机会。

Learning outcome alignment 学习成效对齐

Map each session against your curriculum objectives — scoring how well the AI tutor actually moved the learner toward the intended learning goal, not just whether it generated a response. 将每次会话与课程目标映射对齐，评估 AI 家教真正推动学习者向学习目标进步的程度，而非仅记录是否生成了回复。

Curriculum quality audit 课程质量审查

Continuously audit AI-generated content against your curriculum standards — catching difficulty mismatches, knowledge gaps, and prerequisite failures before they accumulate into learning debt. 持续对照课程标准审查 AI 生成内容，在难度错配、知识缺口与前置失败积累成学习债务之前加以拦截。

Regression suite for model updates 模型迭代回归套件

Every caught failure becomes a regression test case. Model upgrades run against your growing suite before touching learners — so you know what changed, and that it improved things. 每个捕获的失效都成为一个回归测试用例。模型升级在触达学习者之前，先通过持续增长的测试套件——你将清晰知道改变了什么，以及是否真的更好了。

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Ready to make your
AI tutor reliable? 准备好让你的
AI 家教真正可靠了吗？

Tell us about your product and we'll show you exactly where your quality gaps are — and how to close them. 告诉我们你的产品情况，我们将精准定位质量缺口，并给出闭环方案。

Reach out to联系

Roger Yang · Founder

roger@zerore.ai

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Learning depends on AIthat never fails. 学习效果依赖从不失效的 AI。