Overview
Project background description: There are three obvious pain points in dealing with existing wrong questions: students still don’t know how to use them after sorting out the wrong questions, and are prone to "making the same mistake again and again"; it is difficult for teachers to quickly locate weak points in the class after correction, and there is a lack of accurate teaching basis; the school also lacks scientific academic data support, and teaching management still relies on experience. Tools such as Zuoyebang and Xiaoyuan Question Search focus more on answers and analysis, and less on tracking the underlying reasons such as lack of understanding of concepts, inability to use methods, and broken ideas. CauseLink therefore hopes to turn a wrong question on an exam into an explainable and actionable personal learning plan. The core highlight combines answer sheet scanning and exam wrong questions, and AI restores students' mastery of knowledge points in each section and generates a personal "wrong question distribution map." It not only marks "which question is wrong", but also analyzes the underlying reasons behind the loss of points. For example, the loss of points for big questions comes from insufficient practice, unfamiliar methods or repeated calculation errors. Generate a "personalized exam strategy" based on the attribution results, turning abstract suggestions into executable answer sequences and choices for the next exam. Based on the attribution of wrong questions and blanks, mind maps and review documents of the knowledge points tested in this test paper are automatically generated. At the same time, it provides students, teachers and schools with three levels of value: personal review, class weakness and academic data. Project Growth and Reflection Growth and Reflection: This project made me understand that educational products cannot stop at “giving answers”, but must connect data collection, cause judgment and follow-up actions into a closed loop.
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项目背景说明：现有错题处理存在三类明显痛点：学生整理错题后仍不知道怎样使用，容易陷入“一错再错”；教师批改后难以快速定位班级薄弱点，缺少精准教学依据；学校也缺乏科学的学情数据支撑，教学管理仍依赖经验。作业帮、小猿搜题等工具多侧重答案与解析，较少继续追踪概念不理解、方法不会用和思路断裂等深层原因。CauseLink 因此希望把一次考试中的错题转化为可解释、可行动的个人学习方案。核心亮点结合答题纸扫描与考试错题，AI 还原学生对各板块知识点的掌握情况，生成个人“错题分布图”。不只标记“哪题错了”，还分析失分背后的深层原因，例如大题失分来自练习不足、方法不熟或计算反复出错。根据归因结果生成“个性化考场策略”，把抽象建议变成下次考试可执行的答题顺序与取舍。依据错题归因和空白情况，自动生成本次试卷所考知识点的思维导图与复习文档。同时为学生、教师和学校提供个人复盘、班级薄弱点与学情数据三个层级的价值。项目成长与反思成长与反思：这个项目让我理解了教育产品不能停留在“给答案”，而要把数据采集、原因判断和后续行动连成闭环。