| 项目 | 内容 |
|---|---|
| 用途 | 《设计说明书-v0.2.md》P0 完成标准第 2 条：P1 表结构对拍物；开发时按此字段实现 Schema 校验 |
| 性质 | 手工编写样例（非 AI 生成产物），字段以本文件为准，设计书 §6.3 为结构要点 |
| 主案例 | 流浪狗舆情反转事件（NLP 舆情分析与立场检测 + 法学视角：舆情处置、责任认定、言论边界） |
{
"lesson_title": "舆情分析与立场检测——从一次反转事件看 NLP 的局限与责任",
"lesson_type": "案例讨论",
"class_profile_summary": "法学 1 班（42 人）：约 2/3 未接触过代码；数学基础一般；对网络舆情、言论自由类话题兴趣高；约 1/3 有参与网络讨论的经历。待验证假设：对'算法中立'存在普遍误解，需在本课澄清。",
"objectives": [
{
"id": "o1",
"text": "能解释舆情分析的基本流程（采集—清洗—情感/立场分类—趋势）",
"bloom": "理解",
"literacy_dim": "概念理解",
"success_criteria": "能用一句话复述流程，并指出其中至少一个依赖人工判断的环节",
"course_outcome_ids": ["O1-2"]
},
{
"id": "o2",
"text": "能基于舆情数据指出立场检测模型可能出错的两类场景并说明原因",
"bloom": "分析",
"literacy_dim": "批判评价",
"success_criteria": "能结合案例指出'语境缺失''群体标签'两类问题",
"course_outcome_ids": ["O3-1"]
},
{
"id": "o3",
"text": "能就'AI 舆情分析结果能否作为处置依据'给出有法律依据的立场并参与辩论",
"bloom": "评价",
"literacy_dim": "伦理与治理 / 专业情境迁移",
"success_criteria": "发言至少引用一个法律原则（如比例原则、正当程序），并能指出 AI 结论的证据效力问题",
"course_outcome_ids": ["O4-1", "O5-1"]
}
],
"core_plan": {
"core": "舆情分析四环节 + 立场检测原理（不写代码，用流程图）",
"scaffold": "预置'舆情分析词汇卡'（情感/立场/实体/倾向）；讨论前给'法律视角问题清单'",
"extension": "课后选做：用公开工具（如百度舆情演示版）对任一事件做 15 分钟小分析并写反思"
},
"lesson_events": [
{"phase": "pre", "duration": "0min", "teacher_move": "学习通发布课前任务：阅读流浪狗事件时间线（链接）", "student_task": "阅读时间线，写下'舆论为何反转'的初判断", "interaction": "异步", "prompt_questions": []},
{"phase": "in", "duration": "8min", "teacher_move": "开场钩子：展示反转前后两条热搜标题对比", "student_task": "回答'你第一反应信哪个，为什么'", "interaction": "全班投票（举手/学习通投票）", "prompt_questions": ["信息缺什么会让你改变判断？"]},
{"phase": "in", "duration": "15min", "teacher_move": "讲授舆情分析四环节与立场检测原理（含 2 个失败场景：语境缺失、群体标签）", "student_task": "对照时间线找出'模型可能误判'的节点", "interaction": "pair-share", "prompt_questions": ["哪个环节最容易引入偏见？"]},
{"phase": "in", "duration": "20min", "teacher_move": "组织辩论：'AI 舆情分析结果能否作为学校处置依据'（正反方）", "student_task": "按立场分组，用法律原则论证", "interaction": "辩论（正方/反方/观察席）", "prompt_questions": ["比例原则怎么看？", "AI 结论的证据效力？"]},
{"phase": "in", "duration": "5min", "teacher_move": "小结：技术局限 + 法律边界 + 信息素养", "student_task": "写下'我今天改变的一个看法'", "interaction": "exit ticket", "prompt_questions": []},
{"phase": "post", "duration": "0min", "teacher_move": "发布课后任务", "student_task": "必做：反思 300 字；选做：舆情工具小分析", "interaction": "异步", "prompt_questions": []}
],
"hook": "同一件事，一周内两种完全相反的热搜标题——哪个是'真相'？",
"relevance_to_major": "法学学生未来处理网络侵权、名誉权、舆情处置案件时，需要理解 AI 舆情分析的能力边界与证据效力；本课同时训练'技术事实 + 法律判断'的复合能力",
"formative_checks": [
"exit ticket：'我今天改变的一个看法'（o2 证据）",
"辩论发言引用法律原则情况（o3 证据）"
],
"sizheng_integration": [
{
"element": "理性用网 / 信息素养",
"role": "infusion",
"organic_link": "反转事件中学生自身的第一反应即'被情绪带节奏'的样本",
"student_dilemma": "你转发一条'震撼消息'前会核查来源吗？",
"position": "以讨论引导学生反思而非说教",
"avoid_preaching": true
},
{
"element": "法治精神",
"role": "infusion",
"organic_link": "辩论环节'AI 结论能否作为处置依据'天然引出比例原则与正当程序",
"student_dilemma": "证据不足但舆情汹涌时，管理者该听谁的？",
"position": "辩论双方都必须援引法律原则",
"avoid_preaching": true
}
],
"case_plan": {
"case_id": "C7-001",
"usage": "贯穿全课（导入 + 讲解 + 辩论）",
"discussion_ladder": [
"事实层：事件时间线里发生了什么？",
"分析层：哪些节点 AI 舆情分析可能误判？",
"评价层：AI 结论的证据效力与处置依据之争",
"创造层：如果你是学校管理者，如何设计'舆情应对 + 事实核查'流程？"
]
},
"timeline_check": {"total_minutes": 48, "lesson_minutes": 45, "overload": false},
"citations": [
{"source_type": "case", "source_id": "C7-001", "claim_unverified": false},
{"source_type": "teacher_url", "source_id": "U-2026-008", "claim_unverified": false},
{"source_type": "knowledge_point", "source_id": "KP7-3", "claim_unverified": false}
],
"difficulty_notes": "全程不出现代码与公式；'立场检测'以流程图与失败案例呈现；认知层级为理解—评价",
"adaptation_log": [
{"evidence": "画像：2/3 无编程基础 → 决策：核心路径不含代码", "decision": "core 用流程图"},
{"evidence": "画像：对言论自由话题兴趣高 → 决策：主案例选舆情反转并组织辩论", "decision": "课型=案例讨论"}
]
}{
"theme": "案例叙事",
"lesson_type": "case_discussion",
"slides": [
{"slide_type": "cover", "title": "舆情分析与立场检测", "bullets": ["法学 1 班 · 第七章第 2 课时"], "visual": "", "speaker_notes": "开场：先展示反转前后热搜对比（见案例页前）"},
{"slide_type": "agenda", "title": "本课路径", "bullets": ["一个反转事件", "舆情分析四环节", "立场检测的两类失败", "辩论：AI 结论能当依据吗"], "visual": "", "speaker_notes": "预告辩论规则"},
{"slide_type": "case", "title": "事件：一条热搜如何反转", "bullets": ["一周前：'学校为抓流浪狗惹众怒'", "一周后：'流浪狗多次咬伤学生'", "舆论风向 180° 反转"], "visual": "两条热搜截图对比（配图建议区）", "speaker_notes": "引导学生记录第一反应"},
{"slide_type": "concept", "title": "舆情分析四环节", "bullets": ["采集", "清洗", "情感/立场分类", "趋势研判"], "visual": "四环节流程图（diagram_spec）", "diagram_spec": {"kind": "flow", "nodes": ["采集", "清洗", "立场分类", "趋势研判"]}, "speaker_notes": "强调每个环节都有人工判断"},
{"slide_type": "diagram", "title": "立场检测的两类失败", "bullets": ["语境缺失：'咬伤'字面 vs 语境", "群体标签：'爱狗人士'被一刀切"], "visual": "左右对比图（diagram_spec）", "diagram_spec": {"kind": "compare", "left": "语境缺失", "right": "群体标签"}, "speaker_notes": "对照事件时间线找误判节点"},
{"slide_type": "activity", "title": "辩论：AI 结论能否作为处置依据", "bullets": ["正方：效率优先，辅助决策", "反方：证据效力不足，程序正义", "规则：必须援引法律原则"], "visual": "", "speaker_notes": "20 分钟，观察席记录双方论点"},
{"slide_type": "sizheng", "title": "小结：技术边界与我们的责任", "bullets": ["技术能辅助判断，不能替代判断", "转发前多问一句来源", "管理者：程序正义优先"], "visual": "", "speaker_notes": "以讨论收束，不说教；exit ticket 收尾"},
{"slide_type": "summary", "title": "课后", "bullets": ["必做：300 字反思", "选做：舆情工具 15 分钟小分析"], "visual": "", "speaker_notes": "学习通发布"}
]
}{
"cases": [
{
"case_id": "C7-001",
"title": "流浪狗舆情反转事件",
"summary": "某校因捕捉流浪狗被投诉并遭网络舆论批评，校方对涉事保安作出处理；一年后流浪狗多次咬伤学生，舆论风向剧烈反转。事件涉及：舆情生命周期、立场检测的语境依赖、网络治理与法律边界。",
"discussion_questions": [
"时间线上哪个节点舆情出现拐点？",
"如果当时有一个'立场检测模型'，它可能在哪一步出错？",
"学校第一次处置时，AI 舆情分析结果可以作为依据吗？为什么？",
"如果你是校方，如何设计'事实核查 + 舆情回应'流程？"
],
"sizheng_angle": "理性用网：情绪先于事实是常态，技术与制度都要为'真相'服务",
"citations": [{"source_type": "teacher_url", "source_id": "U-2026-008", "claim_unverified": false}]
}
],
"exercises": [
{
"task_type": "recall",
"question": "舆情分析四环节的正确顺序是？",
"options": ["采集—清洗—立场分类—趋势研判", "清洗—采集—趋势研判—立场分类", "采集—立场分类—清洗—趋势研判", "趋势研判—采集—清洗—立场分类"],
"answer": "A",
"tier": "required",
"objective_ids": ["o1"],
"difficulty": "easy",
"knowledge_point": "舆情分析流程"
},
{
"task_type": "explain",
"question": "用本课案例说明'语境缺失'如何导致立场检测出错。",
"answer": "要点：'咬伤学生'在反转前后语境不同（捕杀争议 vs 安全事件），脱离语境的文本分类会把同一表达判为不同立场；参考答案见教师版。",
"tier": "required",
"objective_ids": ["o2"],
"difficulty": "medium",
"knowledge_point": "立场检测的局限"
},
{
"task_type": "case_ruling",
"question": "假定学校第一次处置时收到一份 AI 舆情报告（支持'息事宁人'），校方应否仅凭该报告作出开除保安的决定？请从证据效力与程序角度裁决。",
"answer": "要点：AI 结论属辅助性意见而非定案证据；须遵循比例原则与正当程序，听取当事人陈述；参考答案见教师版。",
"rubric": [{"criterion": "指出 AI 结论非定案证据", "level": "1 分"}, {"criterion": "援引比例原则/正当程序", "level": "1 分"}, {"criterion": "给出可操作流程建议", "level": "1 分"}],
"tier": "required",
"objective_ids": ["o3"],
"difficulty": "hard",
"knowledge_point": "AI 证据效力"
},
{
"task_type": "authentic_task",
"question": "选做：用任一公开舆情/热榜工具，任选事件完成 15 分钟小分析（发生了什么—谁在发言—情绪如何变化—我的核查动作），写成 300 字反思。",
"answer": "无标准答案；教师按 rubric 评价分析完整度与核查意识。",
"rubric": [{"criterion": "事件要素完整", "level": "1 分"}, {"criterion": "识别至少一个立场偏差点", "level": "1 分"}, {"criterion": "有事实核查动作", "level": "1 分"}],
"tier": "extension",
"objective_ids": ["o2", "o3"],
"difficulty": "hard",
"knowledge_point": "舆情分析实践"
}
]
}规则：module_id ∈ {A,B,C,D,E,F} 锁定不可删；每题带 profile_field 与 blueprint_locked；总作答时间 15–20 分钟；学号为独立选填框（非题目，见设计书 §5.5）。
{
"title": "《人工智能基础》学情调查（法学班）",
"description": "本问卷用于了解大家的学习起点与兴趣，帮助老师设计适合本班的内容。全程匿名，学号自愿填写（仅老师可见，不用于成绩、不用于任何模型训练）。",
"module_ids": ["A", "B", "C", "D", "E", "F"],
"questions": [
{"id": "q1", "module_id": "A", "profile_field": "misconception_agency", "blueprint_locked": true, "type": "single", "question": "以下说法你更接近哪一个？", "options": ["AI 已经有自我意识", "AI 只是按规则与统计工作，没有意识", "AI 很快会有意识", "说不清"], "required": true},
{"id": "q2", "module_id": "A", "profile_field": "misconception_omniscience", "blueprint_locked": true, "type": "single", "question": "AI 生成的回答一定正确吗？", "options": ["一定正确", "多数正确但可能出错", "经常出错", "说不清"], "required": true},
{"id": "q3", "module_id": "B", "profile_field": "tool_experience", "blueprint_locked": true, "type": "multi", "question": "你用过以下哪些 AI 工具？（可多选）", "options": ["聊天机器人（如文心/豆包/DeepSeek）", "AI 绘图", "AI 翻译", "AI 搜索", "都没用过"], "required": true},
{"id": "q4", "module_id": "C", "profile_field": "math_basis", "blueprint_locked": true, "type": "single", "question": "以下哪句最符合你的数学情况？", "options": ["高中之后基本没接触数学", "大学学过且不排斥", "数学是我的强项", "提到数学就头疼"], "required": true},
{"id": "q5", "module_id": "C", "profile_field": "programming_basis", "blueprint_locked": true, "type": "scale", "question": "你写过程序或代码吗？（1=完全没写过，5=写过不少）", "required": true},
{"id": "q6", "module_id": "D", "profile_field": "major_prior", "blueprint_locked": true, "type": "single", "question": "你希望这门课多讲哪类内容？", "options": ["AI 在法律行业的应用（如合同审阅、判决预测）", "AI 与言论/舆情的法律问题", "AI 生成内容的责任与版权", "AI 基础原理", "其他（可补充）"], "required": true},
{"id": "q7", "module_id": "E", "profile_field": "attitude_interest", "blueprint_locked": true, "type": "single", "question": "你目前对 AI 的态度是？", "options": ["很感兴趣，希望学深一点", "有点兴趣，想了解够用就行", "没兴趣，为了学分", "有点焦虑，怕被替代"], "required": true},
{"id": "q8", "module_id": "E", "profile_field": "attitude_discussion", "blueprint_locked": true, "type": "single", "question": "你愿意参与课堂讨论/辩论吗？", "options": ["愿意且喜欢", "可以参与但怕说错", "更愿意听讲", "不愿意"], "required": true},
{"id": "q9", "module_id": "F", "profile_field": "expectation_open", "blueprint_locked": false, "type": "text", "question": "你最希望这门课帮你解决什么？（开放题）", "required": false}
]
}学号选填框（非题目，独立呈现）：
<input name="student_no"> 自愿填写，仅老师可见，可留空