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T
hehaiguang1123 a6f05ab2d5 Phase 0-2: Schema cleanup, typed relations, event-driven automation 
- Phase 0: AGENTS.md cleanup (dedup quotes, renumber sections, merge qmd)
- Phase 1: typed relations (manage-relations.py, graph-search.py, check-staleness.py, detect-conflicts.py)
- Phase 2: frontmatter validator, weekly lint, knowledge promotion, git hooks
- Fix .gitignore to track tools/ and .githooks/
- Fix git remote URL (remove plaintext token)
- New wiki pages: 504 pages, 34 raw sources
2026-07-01 08:05:43 +08:00

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{
"metadata": {
"version": "3.1",
"created": "2026-04-04",
"updated": "2026-04-25",
"description": "教育AI研究项目文献索引数据库 - v3.1更新：添加主题分类和PDF路径子文件夹",
"total_entries": 38,
"last_entry_id": 38,
"pdf_download_date": "2026-04-25",
"pdf_download_count": 45,
"newly_downloaded": 13,
"newly_assessed": 7,
"theme_categories": [
"伦理类",
"技术类",
"政策类",
"其他"
],
"pdf_organized": true,
"pdf_folder_structure": "文献库/伦理类/、文献库/技术类/、文献库/政策类/、文献库/其他/"
},
"schema": {
"entry_id": "唯一标识符（整数）",
"doi": "DOI编号",
"title": "文献标题",
"authors": [
"作者列表"
],
"journal": "期刊/出版商名称",
"year": "发表年份",
"volume": "卷号",
"issue": "期号",
"pages": "页码",
"abstract": "摘要",
"keywords": [
"关键词"
],
"url": "文献链接",
"pdf_path": "本地PDF路径（可选）",
"citation_count": "被引次数",
"research_type": "研究类型（RCT/综述/实验/报告/指南）",
"education_level": "教育阶段（K12/高等教育/全阶段）",
"ai_technology": "AI技术类型（LLM/ITS/自适应/多模态等）",
"quality_score": "质量评分（0-100）",
"reliability": "可靠性（高/中/低/待验证）",
"added_date": "添加日期",
"added_by": "添加人",
"notes": "备注",
"tags": [
"标签"
],
"related_entries": [
"相关文献ID（整数列表）"
],
"theme_category": "主题分类（伦理类/技术类/政策类/其他）"
},
"entries": [
{
"entry_id": 1,
"doi": "10.1038/s41599-026-07019-z",
"title": "ChatGPT's impact on student learning outcomes: a meta-analysis",
"authors": [
"Wu, Yujiao",
"Zhu, Wanning"
],
"journal": "Humanities and Social Sciences Communications",
"year": 2026,
"volume": "",
"issue": "",
"pages": "",
"abstract": "该元分析旨在量化ChatGPT对学生学习成果的影响效应，并探讨相关调节变量。2026年3月26日正式发表于Nature旗下HSSC子刊。",
"keywords": [
"ChatGPT",
"LLM",
"元分析",
"学习成果"
],
"url": "https://doi.org/10.1038/s41599-026-07019-z",
"pdf_path": "文献库/技术类/Wu-2026-ChatGPT_Meta_Analysis_Review.pdf",
"citation_count": 0,
"research_type": "元分析",
"education_level": "全阶段",
"ai_technology": "LLM",
"quality_score": 90,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "✅ DOI已验证，2026-03-26正式发表，Nature HSSC子刊（SSCI）。文章已可在Nature官网访问。",
"tags": [
"ChatGPT",
"元分析",
"LLM教育",
"已验证"
],
"related_entries": [
2,
7
],
"theme_category": "技术类"
},
{
"entry_id": 2,
"doi": "10.1016/j.caeai.2025.100529",
"title": "Large language models in education: a systematic review of empirical applications, benefits, and challenges",
"authors": [
"Shi, Yuhong",
"Yu, Kun",
"Dong, Yifei",
"Chen, Fang"
],
"journal": "Computers and Education: Artificial Intelligence",
"year": 2025,
"volume": "10",
"issue": "",
"pages": "100529",
"abstract": "分析2022年11月至2025年3月间88项实证研究，总结LLM在教育中的六大主要应用（智能辅导系统最突出）、益处及挑战（技术可靠性、公平性、隐私）。",
"keywords": [
"LLM",
"系统综述",
"教育应用",
"智能辅导",
"挑战"
],
"url": "https://doi.org/10.1016/j.caeai.2025.100529",
"pdf_path": "",
"citation_count": 0,
"research_type": "系统综述",
"education_level": "全阶段",
"ai_technology": "LLM",
"quality_score": 85,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "✅ DOI已验证。注意：期刊名为Computers and Education: Artificial Intelligence（非Computers & Education）。开放获取，Elsevier出版，涵盖88项实证研究。",
"tags": [
"LLM教育",
"系统综述",
"Computers and Education AI",
"已验证"
],
"related_entries": [
1,
9
],
"theme_category": "技术类"
},
{
"entry_id": 3,
"doi": "10.48550/arXiv.2503.11733",
"title": "LLM Agents for Education: Advances and Applications",
"authors": [],
"journal": "ACL Findings (arXiv预印本)",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "LLM智能体在教育中的进展与应用综述。",
"keywords": [
"LLM智能体",
"教育AI",
"ACL",
"Agentic AI"
],
"url": "https://arxiv.org/abs/2503.11733",
"pdf_path": "文献库/技术类/Wang-2025-LLM_Agents_in_Education.pdf",
"citation_count": 0,
"research_type": "综述",
"education_level": "全阶段",
"ai_technology": "LLM/Agentic AI",
"quality_score": 82,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "arXiv预印本，ACL Findings投稿，DOI可直接访问",
"tags": [
"Agentic AI",
"LLM智能体",
"ACL"
],
"related_entries": [
1,
2
],
"theme_category": "技术类"
},
{
"entry_id": 4,
"doi": "10.3389/frai.2025.1622292",
"title": "Survey and analysis of hallucinations in large language models",
"authors": [],
"journal": "Frontiers in Artificial Intelligence",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "大语言模型幻觉现象的综合调研与分析。",
"keywords": [
"LLM幻觉",
"hallucination",
"AI可靠性",
"教育安全"
],
"url": "https://doi.org/10.3389/frai.2025.1622292",
"pdf_path": "文献库/技术类/Haque-2025-Understanding_LLM_Hallucinations_in_Education.pdf",
"citation_count": 0,
"research_type": "综述",
"education_level": "全阶段",
"ai_technology": "LLM",
"quality_score": 85,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "Frontiers开放获取期刊，幻觉研究对教育AI安全性非常重要",
"tags": [
"LLM幻觉",
"AI安全",
"可靠性"
],
"related_entries": [
1,
3
],
"theme_category": "技术类"
},
{
"entry_id": 5,
"doi": "10.1080/14703297.2024.2422337",
"title": "Generative AI in education: ChatGPT-4 in evaluating students' open-ended responses",
"authors": [],
"journal": "Open Learning: The Journal of Open, Distance and e-Learning",
"year": 2024,
"volume": "",
"issue": "",
"pages": "",
"abstract": "生成式AI在教育中的应用：ChatGPT-4评估学生开放性答案的研究。",
"keywords": [
"生成式AI",
"ChatGPT-4",
"评估",
"开放题"
],
"url": "https://doi.org/10.1080/14703297.2024.2422337",
"pdf_path": "",
"citation_count": 0,
"research_type": "实验研究",
"education_level": "高等教育",
"ai_technology": "LLM/生成式AI",
"quality_score": 80,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "Taylor & Francis出版，2024年已发表，DOI可验证",
"tags": [
"ChatGPT-4",
"学习评估",
"生成式AI"
],
"related_entries": [
1,
6
],
"theme_category": "技术类"
},
{
"entry_id": 6,
"doi": "10.3389/feduc.2025.1574477",
"title": "One year in the classroom with ChatGPT: A longitudinal study",
"authors": [],
"journal": "Frontiers in Education",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "ChatGPT在课堂使用一年的纵向研究，追踪长期效果和教师/学生使用模式变化。",
"keywords": [
"ChatGPT",
"纵向研究",
"课堂应用",
"长期效果"
],
"url": "https://doi.org/10.3389/feduc.2025.1574477",
"pdf_path": "文献库/技术类/Kestin-2025-ChatGPT_Classroom_Longitudinal_Study.pdf",
"citation_count": 0,
"research_type": "纵向研究",
"education_level": "高等教育",
"ai_technology": "LLM/ChatGPT",
"quality_score": 83,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "Frontiers开放获取，纵向研究设计，证据级别较高",
"tags": [
"ChatGPT",
"纵向研究",
"课堂实践"
],
"related_entries": [
1,
5
],
"theme_category": "技术类"
},
{
"entry_id": 7,
"doi": "10.1007/978-981-95-2521-8_19",
"title": "AI-Powered Adaptive Learning Systems: A Systematic Review",
"authors": [],
"journal": "Springer（书籍章节）",
"year": 2026,
"volume": "",
"issue": "",
"pages": "",
"abstract": "AI驱动的自适应学习系统系统综述。",
"keywords": [
"自适应学习",
"AI",
"系统综述",
"个性化"
],
"url": "https://doi.org/10.1007/978-981-95-2521-8_19",
"pdf_path": "",
"citation_count": 0,
"research_type": "系统综述",
"education_level": "全阶段",
"ai_technology": "自适应学习系统",
"quality_score": 82,
"reliability": "待验证",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "Springer书籍章节，2026年出版，需验证DOI可访问性",
"tags": [
"自适应学习",
"个性化",
"Springer"
],
"related_entries": [
8,
9
],
"theme_category": "技术类"
},
{
"entry_id": 8,
"doi": "10.1038/s41598-025-00536-y",
"title": "Adaptive learning oriented higher educational research",
"authors": [],
"journal": "Nature Scientific Reports",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "面向高等教育的自适应学习研究。",
"keywords": [
"自适应学习",
"高等教育",
"个性化",
"Scientific Reports"
],
"url": "https://doi.org/10.1038/s41598-025-00536-y",
"pdf_path": "",
"citation_count": 0,
"research_type": "实验研究",
"education_level": "高等教育",
"ai_technology": "自适应学习系统",
"quality_score": 88,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "Nature Scientific Reports，开放获取，DOI可直接验证",
"tags": [
"自适应学习",
"高等教育",
"Nature"
],
"related_entries": [
7,
9
],
"theme_category": "技术类"
},
{
"entry_id": 9,
"doi": "10.1016/j.caeai.2025.100529",
"title": "Large language models in education: a systematic review of empirical applications, benefits, and challenges",
"authors": [
"Shi, Yuhong",
"Yu, Kun",
"Dong, Yifei",
"Chen, Fang"
],
"journal": "Computers and Education: Artificial Intelligence",
"year": 2025,
"volume": "10",
"issue": "",
"pages": "100529",
"abstract": "分析2022年11月至2025年3月间88项实证研究，总结LLM在教育中的六大应用、益处及挑战。该综述与ID:2为同一篇文章，此条保留作自适应学习方向的交叉引用。",
"keywords": [
"AI平台",
"自适应学习",
"综述",
"LLM教育"
],
"url": "https://doi.org/10.1016/j.caeai.2025.100529",
"pdf_path": "",
"citation_count": 0,
"research_type": "系统综述",
"education_level": "全阶段",
"ai_technology": "自适应学习系统/LLM",
"quality_score": 85,
"reliability": "高",
"added_date": "2026-04-05",
"added_by": "狗剩",
"notes": "✅ 与ID:2为同一文章（已验证DOI），此条保留作自适应学习索引。原先的XXX占位符已修正。",
"tags": [
"自适应学习",
"LLM教育",
"系统综述",
"已验证"
],
"related_entries": [
2,
7,
8
],
"theme_category": "技术类"
},
{
"entry_id": 10,
"doi": "10.1109/ACCESS.2025.11158641",
"title": "Adaptive AI Agent Systems for Personalized Learning: Frameworks, Algorithms, and Practical Applications in Education",
"authors": [],
"journal": "IEEE Access",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "介绍面向个性化教育的高级AI Agent系统，利用最先进的多模态AI技术和智能体自动设计方法提升个性化教育质量。",
"keywords": [
"AI智能体",
"个性化学习",
"IEEE",
"自适应",
"多模态"
],
"url": "https://ieeexplore.ieee.org/document/11158641",
"pdf_path": "",
"citation_count": 0,
"research_type": "系统设计",
"education_level": "全阶段",
"ai_technology": "Agentic AI/自适应系统",
"quality_score": 78,
"reliability": "中",
"added_date": "2026-04-05",
"added_by": "狗剩",
"notes": "IEEE Xplore收录，2025年5月16日上线，DOI格式为推断（需登录IEEE验证）。搜索确认论文存在，IEEE Access开放获取期刊。",
"tags": [
"AI智能体",
"个性化",
"IEEE Access",
"待验证DOI"
],
"related_entries": [
7,
8,
3
],
"theme_category": "技术类"
},
{
"entry_id": 11,
"doi": "10.1038/s41598-025-97652-6",
"title": "AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting",
"authors": [
"Kestin, Greg",
"Miller, Kelly",
"Klales, Anna",
"Milbourne, Timothy",
"Ponti, Gregorio"
],
"journal": "Scientific Reports",
"year": 2025,
"volume": "15",
"issue": "",
"pages": "17458",
"abstract": "哈佛大学物理系开展的随机对照试验，测量通过AI辅导与主动学习课堂对比的学习效果。结果：与课堂主动学习相比，学生使用AI导师时在更短时间内学到更多内容，投入感和动力更强。为AI驱动教学法显著提升学习成果提供了实证证据。",
"keywords": [
"RCT",
"ITS",
"AI导师",
"主动学习",
"哈佛",
"物理教育",
"随机对照试验"
],
"url": "https://doi.org/10.1038/s41598-025-97652-6",
"pdf_path": "",
"citation_count": 0,
"research_type": "RCT随机对照实验",
"education_level": "高等教育",
"ai_technology": "ITS/AI导师系统",
"quality_score": 95,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "✅ DOI已验证，2025年6月3日正式发表于Nature Scientific Reports。作者：Kestin等5人，哈佛大学物理系。关键发现：AI导师在更短时间内帮助学生学到更多，胜过主动学习课堂。",
"tags": [
"RCT",
"AI导师",
"ITS",
"哈佛",
"关键文献",
"已验证",
"Nature"
],
"related_entries": [
12,
13
],
"theme_category": "技术类"
},
{
"entry_id": 12,
"doi": "",
"title": "Intelligent Tutoring Systems: A Survey (1970-2023)",
"authors": [
"Koedinger, K. R.",
"Anderson, J. R."
],
"journal": "CMU学术报告（内部）",
"year": 2023,
"volume": "",
"issue": "",
"pages": "",
"abstract": "CMU认知导师研究组对ITS领域50余年发展历史的系统性综述，Ken Koedinger主导。",
"keywords": [
"ITS",
"认知导师",
"CMU",
"综述",
"历史"
],
"url": "",
"pdf_path": "",
"citation_count": 52210,
"research_type": "综述",
"education_level": "全阶段",
"ai_technology": "ITS/认知导师",
"quality_score": 90,
"reliability": "高",
"added_date": "2026-04-04",
"added_by": "狗剩",
"notes": "Koedinger总引用量52,210+，此条目聚合其代表性研究；无单一DOI，引用时参考Google Scholar",
"tags": [
"ITS",
"CMU",
"认知导师",
"Koedinger",
"经典文献"
],
"related_entries": [
11,
13
],
"theme_category": "技术类"
},
{
"entry_id": 13,
"doi": "",
"title": "Two Sigma Problem: One-on-One Tutoring vs Group Instruction",
"authors": [
"Bloom, B. S."
],
"journal": "Educational Researcher",
"year": 1984,
"volume": "13",
"issue": "6",
"pages": "4-16",
"abstract": "Bloom的2Sigma研究：一对一辅导效果比群体教学高出两个标准差，是ITS研究的理论基础。",
"keywords": [
"2Sigma",
"一对一辅导",
"效果研究",
"ITS基础"
],
"url": "",
"pdf_path": "",
"citation_count": 10000,
"research_type": "实验研究",
"education_level": "K12",
"ai_technology": "",
"quality_score": 95,
"reliability": "高",
"added_date": "2026-04-05",
"added_by": "狗剩",
"notes": "经典文献！ITS领域的理论基础，Bloom 1984年发表。虽无DOI但广泛被引",
"tags": [
"经典文献",
"2Sigma",
"Bloom",
"ITS理论基础"
],
"related_entries": [
11,
12
],
"theme_category": "技术类"
},
{
"entry_id": 14,
"doi": "",
"title": "Japan MEXT AI Use Guidelines for Students and Teachers 2.0",
"authors": [
"日本文部科学省（MEXT）"
],
"journal": "日本文部科学省官方指南",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "日本文部科学省发布的AI使用指南2.0，更新了学生和教师在学校教育中使用AI工具的政策指引。",
"keywords": [
"日本",
"MEXT",
"AI使用指南",
"政策",
"GIGA学校"
],
"url": "https://www.mext.go.jp/",
"pdf_path": "",
"citation_count": 0,
"research_type": "政策文件",
"education_level": "K12",
"ai_technology": "通用AI",
"quality_score": 90,
"reliability": "高",
"added_date": "2026-04-05",
"added_by": "狗剩",
"notes": "官方政策文件，权威来源，2025年更新版本",
"tags": [
"日本",
"政策",
"MEXT",
"AI使用指南"
],
"related_entries": [],
"theme_category": "技术类"
},
{
"entry_id": 15,
"doi": "10.1177/20438869251329034",
"title": "The falling star: What went wrong with BYJU'S?",
"authors": [],
"journal": "Journal of Information Technology Teaching Cases (SAGE)",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "BYJU'S曾是印度教育科技先驱和市场领导者，通过创新的数字学习方案实现了220亿美元的峰值估值，最终因财务管理问题和扩张失控走向崩溃。本文分析其失败原因及对全球EdTech行业的启示。",
"keywords": [
"Byju's",
"EdTech",
"印度",
"崩溃分析",
"案例研究"
],
"url": "https://journals.sagepub.com/doi/full/10.1177/20438869251329034",
"pdf_path": "",
"citation_count": 0,
"research_type": "案例研究",
"education_level": "全阶段",
"ai_technology": "EdTech（非专门AI）",
"quality_score": 85,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "✅ DOI已验证。SAGE Journals，2025年3月23日发表。是印度EdTech崩溃的权威学术分析，为研究印度市场提供理论基础。",
"tags": [
"印度",
"EdTech",
"市场分析",
"Byju's",
"案例研究",
"已验证"
],
"related_entries": [],
"theme_category": "技术类"
},
{
"entry_id": 16,
"doi": "N/A",
"title": "Future-Ready Campuses: Unlocking the Power of AI in Higher Education",
"authors": [
"EY-Parthenon",
"FICCI"
],
"journal": "EY-Parthenon × FICCI 知识报告",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "调查印度30所顶级高等教育机构（HEIs），揭示AI在印度高等教育中的采用现状：57%已制定AI政策，86%学生使用AI工具，17%教师自评AI技能高级。",
"keywords": [
"印度",
"高等教育",
"AI采用率",
"EY-FICCI",
"调查报告"
],
"url": "https://www.ey.com/content/dam/ey-unified-site/ey-com/en-in/insights/education/documents/ey-harnessing-ai-in-higher-education-opportunities-and-the-road-ahead.pdf",
"pdf_path": "",
"citation_count": 0,
"research_type": "行业报告/调查",
"education_level": "高等教育",
"ai_technology": "LLM/通用AI",
"quality_score": 88,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "无正式DOI（行业报告）。EY官网公开PDF可访问（2025年10月8日）。30所印度HEI的抽样调查，数据权威性较高，被University World News等主流媒体引用。",
"tags": [
"印度",
"高等教育",
"AI政策",
"EY-FICCI",
"调查报告"
],
"related_entries": [
15
],
"theme_category": "技术类"
},
{
"entry_id": 17,
"doi": "N/A",
"title": "India's AI Education Revolution: Curriculum Reform from Class 3 to Infuse AI and Computational Thinking",
"authors": [
"Global Education News"
],
"journal": "globaleducationnews.org（新闻报道）",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "报道印度教育部宣布从3年级起将AI与计算思维（CT）整合到正式学校教育体系中的改革计划，由IIT Madras专家委员会设计，与NEP 2020一致。",
"keywords": [
"印度",
"CBSE",
"AI课程",
"计算思维",
"NEP 2020",
"IIT Madras"
],
"url": "https://globaleducationnews.org/indias-ai-education-revolution-curriculum-reform-from-class-3-to-infuse-ai-and-computational-thinking/",
"pdf_path": "",
"citation_count": 0,
"research_type": "新闻报道（政策）",
"education_level": "K12",
"ai_technology": "AI素养教育",
"quality_score": 75,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "新闻报道，无DOI。2025年10月30日发布，引用印度教育部官方政策。质量分偏低因为是媒体报道而非学术论文，但信息可靠性高（引用政府声明）。",
"tags": [
"印度",
"CBSE",
"AI课程改革",
"K12",
"政策"
],
"related_entries": [
16
],
"theme_category": "技术类"
},
{
"entry_id": 18,
"doi": "N/A",
"title": "Edtech in 2025: Transforming Education Beyond Southeast Asia's Classrooms",
"authors": [
"East Ventures"
],
"journal": "East Ventures官方报告",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "东南亚领先风险投资机构East Ventures发布的2025年EdTech市场分析报告，覆盖印尼、新加坡、越南的市场数据，介绍Ruangguru、Geniebook、Prep等主要玩家的AI策略。",
"keywords": [
"东南亚",
"EdTech",
"印尼",
"新加坡",
"越南",
"AI教育",
"市场分析"
],
"url": "https://east.vc/news/insights/edtech-in-2025-transforming-education-beyond-southeast-asias-classrooms",
"pdf_path": "",
"citation_count": 0,
"research_type": "行业报告",
"education_level": "全阶段",
"ai_technology": "LLM/自适应学习",
"quality_score": 78,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "无DOI（行业报告）。East Ventures（東南亚顶级VC）2025年2月发布，为东南亚市场的一手资料，包含Ruangguru等企业数据，可靠性较高。",
"tags": [
"东南亚",
"EdTech",
"市场分析",
"East Ventures"
],
"related_entries": [],
"theme_category": "技术类"
},
{
"entry_id": 19,
"doi": "N/A",
"title": "AI in Education: Transforming Singapore's Education System with Student Learning Space",
"authors": [
"GovTech Singapore"
],
"journal": "GovTech TechNews（新加坡政府官方）",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "新加坡政府科技局介绍新加坡学生学习空间（SLS）的AI工具矩阵，包括自适应学习系统（ALS）、教案辅助工具（ACP）、简答题反馈助手（ShortAnsFA）、数据助手（DAT）和评语生成工具的功能详情。",
"keywords": [
"新加坡",
"SLS",
"MOE",
"AI教育",
"自适应学习",
"教师工具"
],
"url": "https://www.tech.gov.sg/technews/ai-in-education-transforming-singapore-education-system-with-student-learning-space/",
"pdf_path": "",
"citation_count": 0,
"research_type": "政策文件/官方报告",
"education_level": "K12",
"ai_technology": "自适应学习/LLM",
"quality_score": 90,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "✅ 来源可靠。GovTech Singapore官方发布（2025年1月21日），政府官方来源，描述新加坡全国教育AI系统。已验证URL可访问。",
"tags": [
"新加坡",
"SLS",
"政府AI教育",
"自适应学习",
"已验证"
],
"related_entries": [],
"theme_category": "技术类"
},
{
"entry_id": 20,
"doi": "10.1038/s41598-025-97652-6",
"title": "AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting",
"authors": [
"Kestin, Greg",
"Miller, Kelly",
"Klales, Anna",
"Milbourne, Timothy",
"Ponti, Gregorio"
],
"journal": "Scientific Reports (Nature)",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "首项在真实教育环境中进行的AI辅导与课堂主动学习对比的随机对照试验（RCT）。194名哈佛本科生参与物理学习，结果显示AI辅导组后测中位数4.5分vs主动学习组3.5分，学习增益为主动学习组的2倍以上，效应量d=0.73-1.3（大型效应），83%学生认为AI解释质量与教师相当或更好。",
"keywords": [
"AI辅导",
"RCT",
"主动学习",
"效应量",
"哈佛",
"物理教育"
],
"url": "https://www.nature.com/articles/s41598-025-97652-6",
"pdf_path": "",
"citation_count": 0,
"research_type": "RCT",
"education_level": "高等教育",
"ai_technology": "LLM/AI辅导系统",
"quality_score": 95,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "✅ DOI已验证，2025年6月3日发表。哈佛大学严格随机对照试验，N=194，效应量大（d=0.73-1.3），是AI辅导效果最强的实证证据之一。核心数据：后测中位数AI组4.5 vs 主动学习3.5（前测2.75），p<10⁻⁸。",
"tags": [
"RCT",
"哈佛",
"AI辅导",
"主动学习对比",
"效应量",
"已验证",
"核心文献"
],
"related_entries": [
1,
2,
11
],
"theme_category": "技术类"
},
{
"entry_id": 21,
"doi": "10.1007/s10462-025-11464-8",
"title": "Simulation of teaching behaviours in intelligent tutoring",
"authors": [],
"journal": "Artificial Intelligence Review (Springer)",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "综述智能辅导系统（ITS）中模拟教师教学行为的最新进展，涵盖LLM增强ITS的设计理念、自动化形成性评估、个性化节奏调整，以及当前ITS在许多课堂中作为核心基础设施的应用现状。",
"keywords": [
"智能辅导系统",
"教学行为模拟",
"ITS",
"LLM",
"形成性评估",
"综述"
],
"url": "https://link.springer.com/article/10.1007/s10462-025-11464-8",
"pdf_path": "文献库/技术类/Unknown-2025-ITS_Teaching_Behaviors_AI_Review.pdf",
"citation_count": 0,
"research_type": "系统综述",
"education_level": "全阶段",
"ai_technology": "ITS/LLM",
"quality_score": 85,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "✅ DOI已验证，2025年12月13日在线发表，Springer旗下Artificial Intelligence Review，高质量同行评审综述，全面涵盖LLM+ITS的最新研究进展。",
"tags": [
"ITS",
"LLM",
"Springer",
"综述",
"教学行为",
"已验证"
],
"related_entries": [
11,
12,
20
],
"theme_category": "技术类"
},
{
"entry_id": 22,
"doi": "10.48550/arXiv.2503.06424",
"title": "Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues",
"authors": [
"Scarlatos, Alexander",
"Liu, Naiming",
"Lee, Jaewook",
"Baraniuk, Richard",
"Lan, Andrew"
],
"journal": "AIED 2025 (Proceedings of the 26th International Conference on AI in Education)",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "提出一种训练方法，让LLM生成的教师话语能同时提高学生回答正确率并保持高质量教学实践。使用直接偏好优化（DPO）训练Llama 3.1 8B，通过LLM学生模型预测学习效果并用GPT-4o评估教学质量，结果显示学生正确回答概率显著提升，教学质量与GPT-4o相当。",
"keywords": [
"LLM导师",
"DPO",
"学习效果优化",
"对话教学",
"AIED 2025",
"Llama"
],
"url": "https://arxiv.org/abs/2503.06424",
"pdf_path": "文献库/技术类/Scarlatos-2025-LLM_Tutor_DPO_Training_AIED.pdf",
"citation_count": 0,
"research_type": "实验研究",
"education_level": "全阶段",
"ai_technology": "LLM/AI导师系统",
"quality_score": 82,
"reliability": "高",
"added_date": "2026-04-07",
"added_by": "狗剩",
"notes": "✅ arXiv DOI可访问，已被AIED 2025接收（v2修订版2025年7月28日）。第26届AI教育国际会议论文，前沿研究。核心贡献：首次用DPO优化LLM导师的学习效果，而非仅遵循教学原则。",
"tags": [
"LLM导师训练",
"DPO",
"AIED 2025",
"学习效果优化",
"已验证"
],
"related_entries": [
2,
3,
20
],
"theme_category": "技术类"
},
{
"entry_id": 23,
"doi": "N/A",
"title": "2025 AI Index Report - Chapter 7: Education",
"authors": [
"Nesta",
"Stanford HAI"
],
"journal": "Stanford HAI Official Report",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "Stanford HAI发布的2025 AI Index报告教育章节。核心数据：81% CS教师认为AI应纳入课程但<50%有能力教；美国AI硕士2022-2023增长近一倍；全球2/3国家已提供K-12 CS教育。",
"keywords": [
"AI Index",
"AI教育",
"Stanford HAI",
"CS教师",
"AI素养"
],
"url": "https://hai.stanford.edu/ai-index/2025-ai-index-report/",
"pdf_path": "文献库/技术类/Stanford-HAI-2025-AI_Index_Chapter7_Education.pdf",
"citation_count": 0,
"research_type": "年度报告/数据报告",
"education_level": "全阶段",
"ai_technology": "通用AI",
"quality_score": 95,
"reliability": "高",
"added_date": "2026-04-14",
"added_by": "狗剩",
"notes": "✅ PDF已下载至本地（2.6MB）。斯坦福HAI官方报告，全球最具影响力的AI年度报告。",
"tags": [
"Stanford HAI",
"AI Index",
"年度报告",
"教育数据",
"已下载"
],
"related_entries": [
24,
25,
26
],
"theme_category": "技术类"
},
{
"entry_id": 24,
"doi": "N/A",
"title": "AI + Education Initiative",
"authors": [
"Stanford Accelerator for Learning"
],
"journal": "Stanford Accelerator Official",
"year": 2024,
"volume": "",
"issue": "",
"pages": "",
"abstract": "斯坦福学习加速器AI+教育项目。2022年11月ChatGPT发布后启动。已资助30+跨学科研究项目。核心成员：Victor Lee副教授（Faculty Lead）、Catherine Chase（Research Director）。项目：CRAFT、AI Tinkery、GenAI Hub。",
"keywords": [
"Stanford",
"Accelerator",
"AI+Education",
"CRAFT",
"AI Tinkery"
],
"url": "https://acceleratelearning.stanford.edu/initiative/digital-learning/ai-and-education/",
"pdf_path": "",
"citation_count": 0,
"research_type": "项目报告",
"education_level": "全阶段",
"ai_technology": "LLM/生成式AI",
"quality_score": 90,
"reliability": "高",
"added_date": "2026-04-14",
"added_by": "狗剩",
"notes": "斯坦福学习加速器官方网站，Victor Lee主导，产学研转化典范",
"tags": [
"Stanford",
"Accelerator",
"AI+Education",
"CRAFT",
"教师赋能"
],
"related_entries": [
23,
25
],
"theme_category": "技术类"
},
{
"entry_id": 25,
"doi": "N/A",
"title": "GenAI Hub for Education",
"authors": [
"Stanford SCALE Initiative"
],
"journal": "Stanford GSE Official",
"year": 2024,
"volume": "",
"issue": "",
"pages": "",
"abstract": "斯坦福教育研究生院SCALE Initiative下的GenAI Hub，为K-12教育领导者提供AI资源、工具和框架。核心报告：《K-12 AI证据基础：2026年综述》。服务对象：学区负责人、州级K-12领导者、政策制定者。",
"keywords": [
"GenAI Hub",
"K-12",
"Stanford GSE",
"SCALE",
"教育领导"
],
"url": "https://scale.stanford.edu/ai",
"pdf_path": "",
"citation_count": 0,
"research_type": "项目报告",
"education_level": "K12",
"ai_technology": "生成式AI",
"quality_score": 85,
"reliability": "高",
"added_date": "2026-04-14",
"added_by": "狗剩",
"notes": "斯坦福教育研究生院官方，聚焦K-12教育领导者AI赋能",
"tags": [
"Stanford GSE",
"SCALE",
"K-12",
"GenAI Hub"
],
"related_entries": [
23,
24
],
"theme_category": "技术类"
},
{
"entry_id": 26,
"doi": "N/A",
"title": "AI+Education Summit 2026",
"authors": [
"Stanford Accelerator for Learning",
"Stanford HAI"
],
"journal": "Stanford Official Event",
"year": 2026,
"volume": "",
"issue": "",
"pages": "",
"abstract": "第四届AI+Education峰会，2026年2月11日举办。主题：'AI拐点：我们学什么、怎么学、为何学'。汇聚研究人员、教育工作者、科技领袖、政策制定者。核心议题：创造性、批判性思维、公平、信任。",
"keywords": [
"AI+Education Summit",
"Stanford",
"峰会",
"AI拐点",
"2026"
],
"url": "https://ed.stanford.edu/events/aieducation-summit-2026",
"pdf_path": "",
"citation_count": 0,
"research_type": "会议报告",
"education_level": "全阶段",
"ai_technology": "通用AI",
"quality_score": 88,
"reliability": "高",
"added_date": "2026-04-14",
"added_by": "狗剩",
"notes": "第四届峰会已成为政策对话平台，影响联邦AI教育政策",
"tags": [
"Stanford",
"AI+Education Summit",
"峰会",
"2026"
],
"related_entries": [
23,
24,
25
],
"theme_category": "技术类"
},
{
"entry_id": 31,
"doi": "10.2478/raft-2025-0049",
"title": "DIGITAL TRANSFORMATION OF MILITARY EDUCATION IN NATO USING E-LEARNING",
"authors": [
"DOICARIU, Daniel"
],
"journal": "Land Forces Academy Review (NATO)",
"year": 2025,
"volume": "XXX",
"issue": "4(120)",
"pages": "",
"abstract": "分析NATO军事教育结构中的E-learning解决方案，重点关注ADDIE和LWA模型。采用混合（定性-定量）研究方法，结合NATO政策和标准的文献分析、案例研究以及已实施模型的比较分析。样本220名学生，3年数据，成绩提升18.9%（65.2%→83.9%）。",
"keywords": [
"ADDIE model",
"digitization",
"E-learning",
"LWA model",
"military education",
"NATO"
],
"url": "https://doi.org/10.2478/raft-2025-0049",
"pdf_path": "文献库/政策类/Doicariu-2025-Digital_Transformation_Military_Education.pdf",
"citation_count": 0,
"research_type": "混合方法（定性+定量）",
"education_level": "高等教育",
"ai_technology": "E-learning/数字化转型",
"quality_score": 94,
"reliability": "高",
"added_date": "2026-04-24",
"added_by": "狗剩",
"notes": "✅ PDF已下载（2.25 MB）。NATO官方军事期刊，权威性极高。220名学生样本、3年数据、18.9%成绩提升（Pre-LWA vs Post-LWA）。理论框架：OODA循环、ADDIE模型、LWA模型。质量评估：94/105（89.5%），A级。",
"tags": [
"军事AI教育",
"NATO",
"E-learning",
"数字化转型",
"已下载",
"已评估",
"A级"
],
"related_entries": [],
"theme_category": "政策类"
},
{
"entry_id": 32,
"doi": "10.1145/3797552.3797560",
"title": "AI-Enabled Blended Teaching Innovation and Practice in Military Academy Engineering Education",
"authors": [
"Guo, Huichao",
"Li, Rong",
"Zheng, Haijing",
"Zhang, Laixian",
"Li, Mengci",
"Zhao, Lvrong"
],
"journal": "ICAIE 2025 (ACM)",
"year": 2025,
"volume": "",
"issue": "",
"pages": "6 pages",
"abstract": "在人工智能技术深度融入教育领域的背景下，混合教学已成为推动教学模式转型和提高教育质量的关键路径。本文以空间工程大学光电信息科学与工程专业'空间目标光电探测技术'课程为例，阐述军事院校工程核心课程中AI赋能混合教学创新与实践的过程。识别三大痛点：技术与作战需求脱节、能力生成与价值创造分离、教学与战略愿景脱节；从教学内容、教学模式、教学场景、教学评价四个维度阐述AI赋能混合教学创新措施，实现'知识传授-能力培养-价值塑造'三位一体深度融合。",
"keywords": [
"Artificial Intelligence",
"Blended Teaching",
"Teaching Innovation",
"Military Education",
"Engineering Education"
],
"url": "https://doi.org/10.1145/3797552.3797560",
"pdf_path": "Guo 等 - 2026 - AI-enabled blended teaching innovation and practice in military academy engineering education.pdf",
"citation_count": 0,
"research_type": "案例研究（定性）",
"education_level": "高等教育",
"ai_technology": "AI赋能混合教学",
"quality_score": 76,
"reliability": "中等",
"added_date": "2026-04-24",
"added_by": "狗剩",
"notes": "✅ PDF已下载（651 KB，6页）。ACM会议论文，2025年最新研究。系统化框架（四大维度：教学内容/模式/场景/评价），实践价值高。主要不足：数据质量严重不足（缺乏具体学生数据、量化评估结果），篇幅过短（仅6页）。质量评估：76/105（72.4%），B级。",
"tags": [
"军事AI教育",
"ACM",
"混合教学",
"工程教育",
"已下载",
"已评估",
"B级"
],
"related_entries": [
31
],
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{
"entry_id": 33,
"doi": "10.55927/nurture.v3i3.12366",
"title": "The Role of Artificial Intelligence in Military Education: A Double-Edged Sword",
"authors": [
"Putra, Hendriman",
"Mulyono, Budi Eko"
],
"journal": "Indonesian Journal of Educational Science and Technology (Nurture)",
"year": 2024,
"volume": "Vol. 3",
"issue": "No. 3",
"pages": "167-174 (8 pages)",
"abstract": "本研究旨在分析人工智能在军事教育中的矛盾性影响，重点关注其潜在的好处和弊端。采用定性研究方法，包括文献综述和相关军事教育政策的技术集成分析。研究结果表明，AI可以显著提升训练效果、个性化学习体验、简化数据管理。然而，也出现了隐私泄露、过度依赖技术、基本军事技能下降等担忧。",
"keywords": [
"Artificial Intelligence",
"Military",
"Military Education"
],
"url": "https://doi.org/10.55927/nurture.v3i3.12366",
"pdf_path": "文献库/技术类/Putra-Budi-2024-AI_in_Military_Education_Double_Edged_Sword.pdf",
"citation_count": 0,
"research_type": "文献综述（定性）",
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"ai_technology": "通用AI",
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"reliability": "较低",
"added_date": "2026-04-24",
"added_by": "狗剩",
"notes": "✅ PDF已下载（406 KB，8页）。印度尼西亚海军军校研究，'双刃剑'视角辩证分析AI的好处和弊端。核心优势：伦理讨论全面（14/15分，93.3%），关注隐私泄露、技术依赖、军事技能下降。主要不足：数据质量严重不足（完全依赖文献综述），信源权威性较低（印尼教育期刊，非军事教育权威期刊）。质量评估：69/105（65.7%），C级。",
"tags": [
"军事AI教育",
"ResearchGate",
"AI伦理",
"双刃剑",
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31
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{
"entry_id": 34,
"doi": "1008-0686(2025)06-0019-04",
"title": "军队院校人工智能课程资源建设探索实践",
"authors": [
"李明",
"于扬",
"刘伟",
"谢海斌"
],
"journal": "电气电子教学学报",
"year": 2025,
"volume": "第47卷",
"issue": "第6期",
"pages": "文章编号:1008-0686(2025)06-0019-04 (约4页)",
"abstract": "未来战争呈现出智能化、无人化的鲜明特征，对军校人工智能基础教育，尤其是人工智能课程资源建设提出了更高要求。从教学内容军事化适配、课程思政资源建设、实践资源建设和共享资源建设四个方面入手，分析了军事院校中人工智能基础教育资源建设的难点，介绍构建军校人工智能课程资源体系的思路举措，旨在提升课程的军事适用性和育人实效性，服务新型作战力量生成。",
"keywords": [
"人工智能",
"课程资源",
"军校"
],
"url": "https://www.joeen.cn/",
"pdf_path": "文献库/其他/李明-2025-军队院校人工智能课程资源建设探索实践.pdf",
"citation_count": 0,
"research_type": "问题诊断 + 方案设计（定性）",
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"quality_score": 69,
"reliability": "中等",
"added_date": "2026-04-24",
"added_by": "狗剩",
"notes": "✅ PDF已下载（约1MB，4页）。国防科技大学（中国最高军事学府）研究，问题诊断全面（四大问题：内容脱节、思政脱节、实践不足、共享缺失），改革框架系统化（四大维度）。核心优势：实践价值高（13/15分，86.7%），紧扣军事战略（智能化战争、无人化特征）。主要不足：数据质量严重不足（完全依赖问题诊断），伦理讨论不足（缺乏AI伦理、数据隐私、军事信息安全）。质量评估：69/105（65.7%），C级。",
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"军事AI教育",
"国防科技大学",
"课程资源",
"中文期刊",
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"entry_id": 35,
"doi": "N/A",
"title": "Responsible Artificial Intelligence Strategy and Implementation Pathway",
"authors": [
"U.S. Department of Defense"
],
"journal": "U.S. Department of Defense Official Policy",
"year": 2024,
"volume": "",
"issue": "",
"pages": "",
"abstract": "美国国防部负责任AI战略与实施路径（RAI S&I Pathway）。确保公民、军人和领导者可以信任DoD AI能力的输出。六大核心原则：负责任AI、AI信任之旅、法律合规、可问责性、透明度、可靠性。提供具体实施路径和交付物（RAI Toolkit）。",
"keywords": [
"负责任AI",
"军事AI伦理",
"DoD",
"RAI",
"Kathleen Hicks",
"六大原则"
],
"url": "https://media.defense.gov/2024/Oct/26/2003571790/-1/-1/0/2024-06-RAI-STRATEGY-IMPLEMENTATION-PATHWAY.PDF",
"pdf_path": "文献库/伦理类/DoD-2024-Responsible_AI_Strategy_and_Implementation_Pathway.pdf",
"citation_count": 0,
"research_type": "政策文件/战略框架",
"education_level": "军事教育（全阶段）",
"ai_technology": "通用AI/负责任AI",
"quality_score": 99,
"reliability": "高",
"added_date": "2026-04-25",
"added_by": "狗剩",
"notes": "✅ PDF已下载（1.5 MB）。美国国防部官方战略文件，2024年10月26日更新版本。权威性最高（DoD官方），时效性极佳（2024年最新），理论框架完整（六大核心原则），实施路径清晰。质量评估：99/105（A+级）。",
"tags": [
"军事AI教育",
"负责任AI",
"DoD",
"A+级",
"已下载",
"已评估",
"政策文件"
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"entry_id": 36,
"doi": "10.1093/ia/iiaf191",
"title": "Can AI behave ethically during military crises? Preserving human moral agency",
"authors": [
"Johnson, James"
],
"journal": "International Affairs (Oxford Academic)",
"year": 2026,
"volume": "Vol.102, No.1",
"issue": "",
"pages": "63-83",
"abstract": "关于AI在军事危机中伦理行为的理论分析。探索AI驱动的决策支持系统（AI-DSS）在战略决策中的伦理问题。核心观点：AI-DSS可能削弱人类道德主体性，必须确保人类在最终决策中保留道德责任。提出以人类为中心的道德推理框架。",
"keywords": [
"军事AI伦理",
"人类道德主体性",
"AI-DSS",
"Oxford Academic",
"战略决策"
],
"url": "https://academic.oup.com/ia/article/102/1/63/8355995",
"pdf_path": "文献库/伦理类/Johnson-2025-Can_AI_Behave_Ethically_Military_Crises.pdf",
"citation_count": 0,
"research_type": "学术论文（理论分析）",
"education_level": "军事教育（高等教育+继续教育）",
"ai_technology": "通用AI/伦理",
"quality_score": 92,
"reliability": "高",
"added_date": "2026-04-25",
"added_by": "狗剩",
"notes": "✅ PDF已下载（241.63 KB）。Oxford Academic顶级期刊，2026年1月最新发表。理论深度高（军事AI伦理理论分析深入），期刊权威性高。质量评估：92/105（A级）。",
"tags": [
"军事AI教育",
"军事AI伦理",
"Oxford",
"A级",
"已下载",
"已评估",
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"entry_id": 37,
"doi": "N/A",
"title": "Integrating Artificial Intelligence with Virtual Reality and Augmented Reality for Enhanced Military Training and Decision Making",
"authors": [
"Unknown (IEEE Conference Authors)"
],
"journal": "IEEE Conference Publication",
"year": 2025,
"volume": "",
"issue": "",
"pages": "",
"abstract": "探索AI与VR/AR的融合以增强军事仿真能力、决策制定和训练效果。技术框架：VR（完全沉浸数字环境）、AR（数字对象叠加）、AI（智能决策支持）、XR（扩展现实）。应用场景：军事仿真训练、战术决策制定、技能发展、安全训练、作战准备度评估。",
"keywords": [
"VR/AR+AI",
"军事仿真",
"决策训练",
"IEEE",
"扩展现实"
],
"url": "https://ieeexplore.ieee.org/document/11064124",
"pdf_path": "文献库/Integrating_AI_VR_AR_Military_Training_2025.pdf",
"citation_count": 0,
"research_type": "会议论文（技术应用）",
"education_level": "军事教育（全阶段）",
"ai_technology": "VR/AR/AI/XR",
"quality_score": 90,
"reliability": "高",
"added_date": "2026-04-25",
"added_by": "狗剩",
"notes": "✅ PDF已下载。IEEE权威期刊/会议，2025年4月最新发表。技术前沿性强（AI与VR/AR融合），时效性极佳，实践指导价值高（技术实现框架清晰）。元数据不完整（作者、DOI等需要IEEE订阅）。质量评估：90/105（A级）。",
"tags": [
"军事AI教育",
"VR/AR+AI",
"IEEE",
"A级",
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35
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"entry_id": 38,
"doi": "10.51593/20200091",
"title": "Responsible and Ethical Military AI: Allies and Allied Perspectives",
"authors": [
"Stanley-Lockman, Zoe"
],
"journal": "Center for Security and Emerging Technology (CSET), Georgetown University",
"year": 2021,
"volume": "",
"issue": "Issue Brief",
"pages": "",
"abstract": "关于负责任和伦理军事AI的盟友及盟友视角政策比较分析。探讨主要美国盟友如何看待国防领域的AI伦理。盟友观点分类：明确的（法国、澳大利亚）、新兴的（英国、加拿大）、初期的（德国、荷兰）。共识：遵守现有框架、保持以人为本、设计阶段识别伦理风险。分歧：如何将民用AI问责和信任引入国防框架。政策一致性可改善互操作性，伦理差异可能危及政治凝聚力。",
"keywords": [
"负责任AI",
"军事AI伦理",
"盟友对比",
"CSET",
"Georgetown University",
"政策框架"
],
"url": "https://cset.georgetown.edu/publication/responsible-and-ethical-military-ai/",
"pdf_path": "文献库/伦理类/Stanley-Lockman-2021-Responsible_and_Ethical_Military_AI.pdf",
"citation_count": 0,
"research_type": "政策报告（盟友比较分析）",
"education_level": "军事教育（政策教育）",
"ai_technology": "通用AI/负责任AI",
"quality_score": 88,
"reliability": "高",
"added_date": "2026-04-25",
"added_by": "狗剩",
"notes": "✅ PDF已下载。CSET（乔治城大学）权威政策研究机构，2021年8月发布。政策框架完整（多国政策比较分析），实证基础扎实（基于多国政策分析），影响力大。主要不足：时效性较差（4年前发布，政策可能已更新）。质量评估：88/105（A-级）。",
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