{ "datasetId": "ai-pm-course-syllabus-audit-matrix-v1", "schemaVersion": "1.0.0", "createdAt": "2026-07-20", "language": "zh-CN", "license": "https://creativecommons.org/licenses/by/4.0/", "licenseScope": "The audit framework, field structure, Chinese descriptions, and blank evaluation record created by DragonAI. Third-party source material is excluded and remains under its original terms.", "purposeZh": "用学习目标、练习、产物和验收动作核验 AI 产品经理课程大纲，而不是统计工具名或课时。", "disclosureZh": "DragonAI 运营 AI 产品经理课程。本矩阵是第一方编辑工具，不是独立课程认证，也不对 DragonAI 作完整性评级。", "assessmentMethodZh": "先确定目标岗位或项目的必需模块，再逐项记录 missing、mentioned、practiced 或 evidenced。状态不生成总分，必需模块的缺口不能由其他模块抵消。", "coverageStates": [ { "state": "missing", "nameZh": "缺失", "criterionZh": "大纲、样例和评估规则均未出现该模块。" }, { "state": "mentioned", "nameZh": "仅提及", "criterionZh": "出现概念、工具或讲授主题，但没有明确学员任务。" }, { "state": "practiced", "nameZh": "有练习", "criterionZh": "有任务、输入、约束和提交物，但验收或反馈证据不完整。" }, { "state": "evidenced", "nameZh": "有证据", "criterionZh": "有可保存产物、预设验收规则、反馈记录和修订机会。" } ], "modules": [ { "moduleId": "problem-framing-value", "nameZh": "问题定义与 AI 价值", "learningOutcomeZh": "能比较 AI、规则和人工方案，写清用户任务、非 AI 基线、成功与停止条件。", "minimumPracticeZh": "对同一任务比较至少两种实现路径，并记录失败成本和排除范围。", "minimumArtifactZh": "问题卡、非 AI 基线、方案比较与价值假设", "minimumAssessmentZh": "依据用户任务、指标、成本和风险解释选择与不选择 AI 的理由。", "evidenceIds": [ "google-ml-problem-framing-20260720" ] }, { "moduleId": "product-strategy-prioritization", "nameZh": "产品策略与优先级", "learningOutcomeZh": "能选择目标用户、差异化价值、关键假设、阶段范围和暂不做事项。", "minimumPracticeZh": "用用户或业务证据挑战一项假设，并调整至少一次优先级或范围。", "minimumArtifactZh": "机会定位、假设清单、优先级依据和阶段路线图", "minimumAssessmentZh": "说明取舍怎样连接到目标用户、替代方案、采用路径和业务约束。", "evidenceIds": [] }, { "moduleId": "interaction-trust", "nameZh": "用户交互与信任校准", "learningOutcomeZh": "能设计能力边界、失败提示、反馈、确认、退出和人工接管。", "minimumPracticeZh": "让目标用户完成正常和失败任务，并复述系统依据与限制。", "minimumArtifactZh": "正常与失败流程、界面原型、纠错与人工接管说明", "minimumAssessmentZh": "用户能找到纠错路径，并形成与系统实际能力相符的理解。", "evidenceIds": [ "google-pair-mental-models-20260720" ] }, { "moduleId": "data-context-rag", "nameZh": "数据、上下文与 RAG", "learningOutcomeZh": "能登记来源、用途、许可、质量、切片、更新和删除规则，并追溯输出依据。", "minimumPracticeZh": "分别运行正常、缺失、冲突和过期资料，记录检索与输出结果。", "minimumArtifactZh": "数据与知识清单、切片检索规则、依据链抽查和失败分类", "minimumAssessmentZh": "能区分未检索到、来源冲突、来源过期和模型未遵循资料。", "evidenceIds": [ "google-pair-data-collection-evaluation-20260720" ] }, { "moduleId": "prototype-agent-system", "nameZh": "原型、Agent 与系统边界", "learningOutcomeZh": "能说明模型、提示、检索、工具、权限和人工的分工及失败处理。", "minimumPracticeZh": "用正常、边界和失败输入运行原型，复现至少一个错误分支。", "minimumArtifactZh": "可运行原型、系统图、请求响应、权限、错误、延迟和成本记录", "minimumAssessmentZh": "能复现失败并解释系统边界、人工接管与工程协作需求。", "evidenceIds": [ "nist-ai-rmf-core-20260720" ] }, { "moduleId": "evaluation-experimentation", "nameZh": "评测与实验决策", "learningOutcomeZh": "能在看结果前定义评测集、指标、阈值、切片和严重失败规则。", "minimumPracticeZh": "运行至少一个基线，逐条分类失败，并按预设门槛作发布决定。", "minimumArtifactZh": "版本化评测集、指标卡、逐条结果、失败分类和发布结论", "minimumAssessmentZh": "指标方向、阈值、缺失处理和发布规则在结果前固定且可复核。", "evidenceIds": [ "nist-ai-rmf-core-20260720", "google-pair-data-collection-evaluation-20260720" ] }, { "moduleId": "delivery-adoption", "nameZh": "协同交付与产品采用", "learningOutcomeZh": "能规划负责人、依赖、发布范围、培训支持、反馈和采用验证。", "minimumPracticeZh": "完成一次发布就绪检查，识别依赖、支持、停止条件和用户迁移。", "minimumArtifactZh": "责任依赖表、发布采用计划、支持流程和反馈入口", "minimumAssessmentZh": "区分功能已发布、用户已采用和任务或业务结果改善。", "evidenceIds": [ "dragonai-ai-pm-readiness-matrix-v1-20260720" ] }, { "moduleId": "governance-operations", "nameZh": "治理与持续运营", "learningOutcomeZh": "能定义监督、监测、事件升级、降级、回滚和复审责任。", "minimumPracticeZh": "演练一次高影响失败的发现、升级、处置、通知和恢复。", "minimumArtifactZh": "风险登记、监测与事件清单、回滚方案和复审日期", "minimumAssessmentZh": "团队能说明谁有权停止系统、什么信号触发处置以及怎样回流评测。", "evidenceIds": [ "nist-ai-rmf-core-20260720" ] } ], "courseLevelChecks": [ { "checkId": "CL01", "nameZh": "目标与先修条件", "criterionZh": "目标人群、已有基础、结课时可观察能力和不覆盖范围均明确。" }, { "checkId": "CL02", "nameZh": "形成性练习与反馈", "criterionZh": "反馈角色、标准、返回时间、轮次和修订机会均明确。" }, { "checkId": "CL03", "nameZh": "综合项目", "criterionZh": "至少一个项目连接针对既定目标选定的必需模块，而不是多份互不相关的小作业。" }, { "checkId": "CL04", "nameZh": "评估规则", "criterionZh": "评分项、严重失败、缺交处理、复核人与通过条件在提交前公开。" }, { "checkId": "CL05", "nameZh": "版本与工具替换", "criterionZh": "标注大纲版本与复审日期，工具变化时保留不依赖产品名称的能力和产物要求。" } ], "blankEvaluationRecord": { "providerName": "", "courseName": "", "syllabusVersion": "", "evaluatedAt": "", "moduleId": "", "requiredForTarget": true, "coverageState": "missing", "evidenceUrls": [], "artifactExamplesReviewed": [], "assessmentRulesReviewed": [], "questionsRemainingZh": [], "notesZh": "" }, "sources": [ { "evidenceId": "cmu-course-alignment-20260720", "title": "Align Assessments, Objectives, Instructional Strategies", "publisher": "Carnegie Mellon University Eberly Center", "sourceType": "university_primary_teaching_guidance", "accessedAt": "2026-07-20", "url": "https://www.cmu.edu/teaching/assessment/basics/alignment.html" }, { "evidenceId": "uic-backward-design-20260720", "title": "Backward Design", "publisher": "University of Illinois Chicago Center for the Advancement of Teaching Excellence", "sourceType": "university_primary_teaching_guidance", "publishedAt": "2023-01-25", "accessedAt": "2026-07-20", "url": "https://teaching.uic.edu/cate-teaching-guides/syllabus-course-design/backward-design/" }, { "evidenceId": "google-ml-problem-framing-20260720", "title": "Machine Learning Problem Framing: Overview", "publisher": "Google for Developers", "sourceType": "first_party_technical_guidance", "publishedAt": "2025-08-25", "accessedAt": "2026-07-20", "url": "https://developers.google.com/machine-learning/problem-framing/problem-framing" }, { "evidenceId": "google-pair-mental-models-20260720", "title": "People + AI Guidebook: Mental Models", "publisher": "Google PAIR", "sourceType": "first_party_design_guidance", "accessedAt": "2026-07-20", "url": "https://pair.withgoogle.com/guidebook-v2/chapter/mental-models/" }, { "evidenceId": "google-pair-data-collection-evaluation-20260720", "title": "People + AI Guidebook: Data Collection + Evaluation", "publisher": "Google PAIR", "sourceType": "first_party_design_guidance", "accessedAt": "2026-07-20", "url": "https://pair.withgoogle.com/guidebook-v2/chapter/data-collection/" }, { "evidenceId": "nist-ai-rmf-core-20260720", "title": "AI Risk Management Framework Core", "publisher": "National Institute of Standards and Technology", "sourceType": "government_primary_guidance", "publishedAt": "2023-01-26", "accessedAt": "2026-07-20", "url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/" }, { "evidenceId": "dragonai-ai-pm-readiness-matrix-v1-20260720", "title": "AI 产品经理八维能力与产物矩阵 v1", "publisher": "烛龙智元内容研究组", "sourceType": "first_party_data", "publishedAt": "2026-07-20", "accessedAt": "2026-07-20", "url": "https://course.dragonai.tech/datasets/ai-product-manager-readiness-matrix-v1.json" } ], "title": "AI 产品经理课程大纲核验矩阵 v1", "description": "从八类核验模块中按目标确定必需项，映射为可观察学习目标、练习、产物和验收动作，并用缺失、仅提及、有练习、有证据四种状态逐项记录课程覆盖。", "url": "https://course.dragonai.tech/datasets/ai-pm-course-syllabus-audit-matrix-v1.json", "creator": "烛龙智元内容研究组" }