{
  "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
      ],
      "theme_category": "技术类"
    },
    {
      "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）",
      "education_level": "高等教育",
      "ai_technology": "通用AI",
      "quality_score": 69,
      "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",
        "已下载",
        "已评估",
        "C级"
      ],
      "related_entries": [
        31
      ],
      "theme_category": "技术类"
    },
    {
      "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）",
      "education_level": "高等教育",
      "ai_technology": "课程资源建设",
      "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。",
      "tags": [
        "军事AI教育",
        "国防科技大学",
        "课程资源",
        "中文期刊",
        "已下载",
        "已评估",
        "C级"
      ],
      "related_entries": [
        31
      ],
      "theme_category": "其他"
    },
    {
      "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"
      ],
      "related_entries": [],
      "theme_category": "伦理类"
    },
    {
      "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",
        "学术论文"
      ],
      "related_entries": [
        35
      ],
      "theme_category": "伦理类"
    },
    {
      "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",
        "已下载",
        "已评估",
        "会议论文"
      ],
      "related_entries": [
        35
      ],
      "theme_category": "技术类"
    },
    {
      "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）。",
      "tags": [
        "军事AI教育",
        "负责任AI",
        "CSET",
        "A-级",
        "已下载",
        "已评估",
        "政策报告"
      ],
      "related_entries": [
        35
      ],
      "theme_category": "伦理类"
    }
  ],
  "indexes": {
    "by_year": {
      "1984": [
        13
      ],
      "2023": [
        12
      ],
      "2024": [
        5,
        33
      ],
      "2025": [
        2,
        3,
        4,
        6,
        8,
        9,
        10,
        11,
        14,
        15,
        16,
        17,
        18,
        19,
        20,
        21,
        22,
        31,
        32,
        34
      ],
      "2026": [
        1,
        7
      ]
    },
    "by_journal": {
      "Humanities and Social Sciences Communications": [
        1
      ],
      "Nature Scientific Reports": [
        8,
        11,
        20
      ],
      "Computers and Education: Artificial Intelligence": [
        2,
        9
      ],
      "Frontiers in Artificial Intelligence": [
        4
      ],
      "Frontiers in Education": [
        6
      ],
      "IEEE Access": [
        10
      ],
      "Springer (Artificial Intelligence Review)": [
        7,
        21
      ],
      "ACL Findings": [
        3
      ],
      "AIED 2025 / arXiv": [
        22
      ],
      "Educational Researcher": [
        13
      ],
      "CMU学术报告": [
        12
      ],
      "日本文部科学省官方指南": [
        14
      ],
      "Open Learning": [
        5
      ],
      "SAGE Journals": [
        15
      ],
      "EY-Parthenon×FICCI行业报告": [
        16
      ],
      "新闻报道（政策）": [
        17
      ],
      "East Ventures行业报告": [
        18
      ],
      "GovTech Singapore官方": [
        19
      ],
      "ICAIE 2025 (ACM)": [
        32
      ],
      "Indonesian Journal of Educational Science and Technology (Nurture)": [
        33
      ],
      "电气电子教学学报": [
        34
      ],
      "Land Forces Academy Review (NATO)": [
        31
      ]
    },
    "by_technology": {
      "LLM": [
        1,
        2,
        3,
        4,
        5,
        6,
        16,
        19,
        20,
        22
      ],
      "自适应学习系统": [
        7,
        8,
        9,
        19
      ],
      "ITS/AI导师系统": [
        11,
        12,
        13,
        20,
        21
      ],
      "Agentic AI": [
        3,
        10
      ],
      "通用AI": [
        14,
        16,
        33
      ],
      "AI素养教育": [
        17
      ],
      "EdTech市场": [
        15,
        18
      ],
      "AI赋能混合教学": [
        32
      ],
      "E-learning/数字化转型": [
        31
      ],
      "课程资源建设": [
        34
      ]
    },
    "by_education_level": {
      "K12": [
        13,
        14,
        17,
        19
      ],
      "高等教育": [
        5,
        6,
        8,
        11,
        16,
        20,
        31,
        32,
        33,
        34
      ],
      "全阶段": [
        1,
        2,
        3,
        4,
        7,
        9,
        10,
        12,
        15,
        18,
        21,
        22
      ]
    },
    "by_reliability": {
      "高": [
        1,
        2,
        3,
        4,
        5,
        6,
        8,
        9,
        11,
        12,
        13,
        14,
        15,
        16,
        17,
        18,
        19,
        20,
        21,
        22,
        31
      ],
      "中": [
        10,
        32,
        34
      ],
      "较低": [
        33
      ],
      "待验证": [
        7
      ]
    },
    "by_region": {
      "全球": [
        1,
        2,
        3,
        4,
        7,
        8,
        9,
        10,
        11,
        12,
        13,
        21,
        22
      ],
      "印度": [
        15,
        16,
        17
      ],
      "东南亚": [
        18,
        19
      ],
      "日本": [
        14
      ],
      "美国": [
        5,
        6,
        20
      ],
      "欧洲": [
        31
      ],
      "中国": [
        34
      ],
      "亚洲（东南亚除外）": [
        33
      ]
    },
    "by_theme_category": {
      "伦理类": [
        35,
        36,
        38
      ],
      "技术类": [
        1,
        3,
        4,
        6,
        21,
        22,
        23,
        33
      ],
      "政策类": [
        31
      ],
      "其他": [
        34
      ]
    }
  },
  "statistics": {
    "total_by_year": {
      "1984": 1,
      "2023": 1,
      "2024": 2,
      "2025": 20,
      "2026": 2
    },
    "total_by_journal": {
      "Nature系列": 4,
      "Frontiers系列": 2,
      "Computers and Education AI": 2,
      "IEEE": 1,
      "Springer": 2,
      "ACL/arXiv/AIED": 2,
      "政策文件/报告": 5,
      "行业报告": 2,
      "新闻报道": 1,
      "SAGE Journals": 1,
      "ACM会议": 1,
      "其他期刊": 5
    },
    "total_by_technology": {
      "LLM": 10,
      "自适应学习系统": 4,
      "ITS/AI导师": 5,
      "Agentic AI": 2,
      "通用AI/政策": 4,
      "AI素养教育": 1,
      "EdTech市场": 2,
      "军事AI教育": 4,
      "E-learning/数字化转型": 1,
      "AI赋能混合教学": 1,
      "课程资源建设": 1
    },
    "high_quality_entries": 21,
    "medium_quality_entries": 3,
    "need_verification": 1,
    "classic_papers": 3,
    "doi_verified": 19,
    "doi_estimated": 1,
    "no_doi_reports": 5,
    "last_verification_date": "2026-04-07",
    "new_entries_this_week": 11,
    "new_entries_this_session": 9,
    "regional_coverage": {
      "全球通用": 11,
      "美国": 3,
      "欧洲": 1,
      "中国": 1,
      "日本": 1,
      "印度": 3,
      "东南亚": 2,
      "亚洲（东南亚除外）": 1
    }
  }
}