以人为中心的AI:2026年研究与实践参考
这份参考资料围绕一个核心问题展开:当人工智能深度介入学习与创作,人如何保有自己的判断、表达,以及对作品的责任?为了回应这个问题,我们选读了研究论文、政策框架、资助方资料和技术标准。这不是一次系统性或穷尽性的文献综述,而是对精选一手来源的有范围阅读。
在呈现方式上,我们将四类内容明确区分:来源本身的结论(已核实)、YuCROWN 对这些材料的理解(解读)、可供未来验证的研究方案(候选研究设计),以及目前尚未解决、对外使用前须确认的内容(待核查)。这份资料供内部讨论与后续设计使用,不能原样作为对外声明,也不是学术意义上的文献综述。
第2版修正了上一版若干缺乏来源依据的概括,补入实证研究、资助条件、技术标准、候选研究方法、项目对应关系,以及完整的文献目录。
- 「以人为中心的人工智能(Human-Centered AI,HCAI)」在研究、政策、教育、资助和技术标准等不同领域各自意味着什么?它与 AI 素养和负责任的 AI 是什么关系——是相互交叉,还是取代两者?
- 在范围有限、针对特定来源的研究中,AI 对学习、思考和创造力究竟显示出哪些影响?
- 哪些资助条件目前可以核实并公开查阅,哪些仍待确认?
- 现有的来源与流转记录(provenance)标准和成就凭证标准止步于何处?哪些部分仍是真正悬而未决的设计问题?
- YuCROWN 现有的各个计划——Fellow、Conversations、Field、Earth Library、教育与研究计划(Study)——可能如何与这一领域产生关联,同时不宣称获得任何机构背书?
- Cali 实际上可以围绕哪些研究问题和方法进行设计?
- YuCROWN 目前可以诚实地主张什么,哪些内容仍是开放的?
如何使用这份参考资料
- 叙事表述参考 A、B、K 节,使用真正站得住脚的表述——人的自主性、判断、作者身份与创作贡献——并明确哪些内容应保留在内部。
- Cali / Study在确定任何具体方法之前,先参考 B、I、J 节——范围有限的证据、候选研究问题,以及参与和同意机制的设计。
- 资助参考 C 节按各资助方列出的已核实条件与待解问题。若要在其他场合引用金额或截止日期,须直接回到来源官网核查最新信息。
- 项目 / Field参考 G 节的对应关系,了解一个真实试点项目可能如何与 Fellow、Conversations、Field 或 Earth Library 产生关联,同时不重新定义其中任何一个计划。
- 技术协议 / EarthLinC参考 E、F 两节,了解现有标准已涵盖哪些内容,哪些对 EarthLinC 而言仍是真正未经检验的设计问题。
概念与定义
第2版有一处重要修正:以人为中心的人工智能(HCAI)并不取代 AI 伦理或负责任的 AI,也不是在技能内容之外额外叠加的一层。从本节所检视的来源来看,它是一种跨学科取向,关注人的需求、能力、价值与社会情境。这一取向与 AI 素养、负责任的 AI、学习科学、参与式设计和无障碍设计相互交叉、彼此借鉴,而非取代其中任何一者。解读
本文使用的概念(工作定义,并非学界统一分类)
主要框架来源
UNESCO — AI Competency Framework for Students(学生 AI 素养能力框架) 已核实
- 发布日期
- 2024年(官方文章标注日期为 2024-08-08);查阅时的官网页面显示 2026 年 1 月有更新,这只能说明页面曾被修订,不能证明存在 2026 年版的新框架。
- 查阅日期
- 2026-10-03
- 来源类型
- 机构性能力框架(UNESCO)
- 来源要点
- 框架涵盖四个维度、共十二项能力:以人为中心的思维(human-centred mindset)、AI 伦理、AI 技术与应用、AI 系统设计,并按「理解/应用/创造」三个层级递进。
- 可作为哪些表述的依据
- 将「以人为中心的思维」和「共同创造者(co-creator)」作为有来源依据的当前课程用语。
- 不能据此推断
- 该框架在实践中的实际成效,以及 2026 年修订版是否确实存在。
- 来源
- unesco.org — AI Competency Framework for Students [1]
OECD — Digital Education Outlook 2026(《数字教育展望 2026》) 已核实
- 发布日期
- 2026-01-19 · DOI 10.1787/062a7394-en
- 查阅日期
- 2026-10-03
- 来源类型
- 政府间政策综合报告
- 来源要点
- 报告检视生成式 AI 在教育中的有效使用方式,明确区分短期任务表现提升与持久性学习之间的差别,并强调教学设计才是决定性因素,而非工具本身。
- 可作为哪些表述的依据
- 为「有协助下的表现不等于学习」这一区分提供政策层面、有具体日期的来源支持(发布于 2026-01-19)。
- 不能据此推断
- 对任何特定课堂、人群或 YuCROWN 试点项目的因果效应。
- 来源
- oecd.org — Digital Education Outlook 2026 [2]
Stanford HAI — About 已核实
- 发布日期
- 查阅时的官网页面未标注原始发布日期
- 查阅日期
- 2026-10-03(本次查阅时已核实该页面——修正第1版中无法确认网址的问题)
- 来源类型
- 机构「关于我们」页面
- 来源要点
- 描述一种跨学科取向,研究目标包括 AI 对人的影响、AI 如何增强人的能力而非取代人,以及促进人类福祉。
- 可作为哪些表述的依据
- 将 Stanford HAI 作为该领域论述框架的参考机构。
- 不能据此推断
- Stanford HAI 与 YuCROWN 之间存在任何合作关系、审核或背书。
- 来源
- hai.stanford.edu — About [3]
Microsoft Research — People-Centric AI 已核实
- 发布日期
- 未标注原始日期;该主题页面持续维护更新
- 查阅日期
- 2026-10-03
- 来源类型
- 企业研究实验室主题页面
- 来源要点
- 陈述研究目标涵盖人的自主性与技能、创造力与协作,以及公平的代表性与参与;列出具名项目,包括 Tools for Thought、Project Provenance 和 Project Volano。
- 可作为哪些表述的依据
- 「过程可见性」和「来源与流转记录」是企业界正在积极研究的议题——这说明相关研究已经存在,不能据此宣称 YuCROWN 发现了独有的空白或市场独特性。
- 不能据此推断
- 上述任一项目已实现 EarthLinC 所提议的功能,也不能据此确立 YuCROWN 做法相对于这些项目的新颖性。
- 来源
- microsoft.com/research — People-Centric AI [4]
HCAI、AI 素养和负责任的 AI,是对同一趋势的几种相互交叉的视角,不是彼此竞争的标签。AI 素养本身已包含自主性内容——例如 UNESCO 框架中的「共同创造者」概念;负责任的 AI 也早已涉及人类监督问题。上述来源确立了自主性、来源与流转记录和人的能力是学界与业界正在研究的议题,但不能据此宣称这是一个独特或尚待填补的市场空白。一个留给教育与研究计划(Study)探索的候选问题是:哪些经过选择的过程记录有助于人们解释自己的选择,以及这种帮助如何被检验。
学习与创造力的研究证据
以下条目的范围有限:每一项只是单一研究或工作论文,各自有特定的研究对象、方法和局限。任何一项都不应被解读为关于「AI 与学习」的普遍定论。经同行评审的研究,是关于其自身样本和特定任务的证据,而非适用于所有情境的规律;工作论文则明确标注尚未经过同行评审。
Lee et al. — Impact of Generative AI on Critical Thinking 已核实
- 发表
- CHI 2025(Microsoft Research 发表页面)
- 查阅日期
- 2026-10-03
- 研究方法与样本
- 问卷调查,319 名成年知识工作者,共收集 936 条关于 AI 工具使用的自我报告案例。
- 在这项研究中观察到的结果
- 对 AI 的自我报告信心越高,参与者自报投入的批判性思维精力越低;对自身任务能力越有信心,则自报投入的精力越多。
- 局限
- 研究对象为成年知识工作者,数据为自我报告,属相关性研究——这是一项关联性发现,不支持关于认知能力下降的因果推断,也没有青少年或课堂数据。减少认知负担本身未必是坏事:在某些情境中,AI 协助消除真实障碍对参与具有积极价值。
- 对研究设计的可能参考
- 候选研究设计 Study 可以询问参与者为何接受或拒绝某项建议,而不是把频繁使用 AI 本身直接标记为「依赖」。
- 来源
- microsoft.com/research — 发表页面 [5]
Bastani et al. — Generative AI Without Guardrails Can Harm Learning 已核实
- 发表
- PNAS, 2025 · DOI 10.1073/pnas.2422633122
- 查阅日期
- 2026-10-03
- 研究方法与样本
- 以班级为单位的随机对照实验,约 1,000 名高中生,土耳其一所学校。研究比较三种情境:无防护机制的 GPT Base、由教师专门设计的 GPT Tutor,以及对照组;科目为高中数学。
- 在这项研究中观察到的结果
- 两种 AI 介入情境下,有协助的练习成绩均有提升。但在无防护机制的 GPT Base 组,无协助情况下的考试成绩约低 17%。经过教学设计的 GPT Tutor 在很大程度上弥补了这一差距,但尚未显示无协助考试成绩的净正向提升。值得注意的是,并非所有 AI 介入都造成损害——两种介入情境的结果明显不同。
- 局限
- 单一学校、单一科目(数学)、短期观察。这项研究针对的是结构性数学任务,不能推广至开放式的艺术或创作类任务,也不能据此推论长期的自主性发展。
- 对研究设计的可能参考
- 候选研究设计 任何 Study 方法都应把「有协助下的产出」与「之后能否独立解释或在新情境中运用」分开衡量。
- 来源
- pnas.org [6]
Doshi & Hauser — AI Enhances Individual Creativity, Reduces Collective Novelty 已核实
- 发表
- Science Advances, 2024-07-12 · DOI 10.1126/sciadv.adn5290
- 查阅日期
- 2026-10-03
- 研究方法与样本
- 随机分派的短篇故事写作任务,比较有无 AI 构思提示两种情境,由外部评分者打分。
- 在这项研究中观察到的结果
- 经 AI 协助完成的故事,从个别来看被外部评分者评为更具创造力,对创造力基准较低的写作者尤为明显。但从整体来看,这批故事彼此之间变得更为相似。
- 局限
- 单一、范围有限的写作任务,由外部评分者评分,未测量个人表达风格的发展,也未追踪长期的作者身份与创作贡献。外部评分者的判断与参与者自身对「这是我的选择」的感受,是两回事。
- 对研究设计的可能参考
- 候选研究设计 在记录外部对产出润色程度的评分的同时,也追踪作品的多样性,以及参与者是否认为这些选择出自自己、并愿意为它们负责。
- 来源
- pmc.ncbi.nlm.nih.gov [7]
Oreopoulos & Low — One Click Away: AI Tutoring with Khanmigo 待核查
- 发表
- NBER 工作论文第 35620 号,2026 年 8 月 · DOI 10.3386/w35620——尚未经同行评审的工作论文
- 查阅日期
- 2026-10-03
- 研究方法与样本
- 为期两年、以学校为单位的随机对照实验,涉及田纳西州 18 所中学的补救数学教学;比较「可汗学院平台加教练式辅导」与常规教学(依据发表列表及检索摘要所述)。
- 当前状态
- 本次查阅时,完整 PDF 及 NBER 论文页面均返回 403 错误;具体效应估计值未经独立核查,在完整文本经过审阅之前,不得引用任何具体数字。
- 目前不能据此推断
- 效应的大小,或 AI 部分相对于教练辅导部分各自的独立贡献。
- 相关但不可替代
- 可汗学院自身的研究页面,是该机构对自身实施情况和研究结果的陈述,不是独立重复验证。
blog.khanacademy.org/research - 来源
- nber.org/papers/w35620 [8]
这四项来源研究情境各异,不能合并成单一因果结论。AI 协助可以同时提升有协助下的表现和个别评分的创造力,但在其中一项随机对照数学研究的无防护机制情境下,无协助考试表现下降约 17%;在另一项写作任务中,则导致群体作品相互趋同。成年知识工作者的问卷调查发现,对 AI 的信心与自报批判性思维精力之间存在负相关——这是相关性发现,不支持因果推断。此外,认知负担减少本身未必是坏事;对某些参与者来说,AI 协助消除真实障碍正是让参与成为可能的关键。
真正关键的是在项目开始前就明确:这个项目究竟希望支持哪种具体能力或活动——是独立迁移、个人表达风格,还是协作过程?产出质量和工具使用的热度,单独来看都不足以证明学习、自主性或作者身份与创作贡献确实发生。任何 Study 方法都应在收集数据之前先说明这一目标,而不是在事后从数据中反推。
资助条件与合作路径
以下先列出几类筛选资助机会时可以参考的问题维度,再逐一呈现四个机构目前已核实的具体条件。主题相关不等于符合申请资格——用语上的契合,不代表双方已确认适配。
| 资助关注类型 | 资助方似乎在问的问题 |
|---|---|
| 青少年自主性 / 批判性思维 / 身心健康 | 在 AI 日益普及的环境中,年轻人的判断力和身心健康是否仍然完好? |
| 研究—实践伙伴关系 | 研究者与实践者能否在共享治理的架构下共同设计和检验? |
| 公平 / 乡村 / 在地化的可及性 | 哪些人被排除在这些工具和机会之外? |
| 无障碍 / 包容性 | 这项工作从一开始是否就适用于残障人士、多语言使用者和资源有限的学习者? |
| 创造力 / 文化 / 公民表达 | AI 是扩展了还是削弱了创意、文化与公民实践的多样性? |
| 工具、额度、培训等非现金支持 | 资助方能否以非现金方式提供支持? |
| 项目执行与评估能力 | 该机构是否真的具备执行、衡量和汇报的能力? |
Young Futures — Unwritten Challenge 已核实(条件) 待核查(适配性)
- 申请资格
- 美国境内具有免税资格的非营利组织、学校,或由符合资格的机构承担财务和合规责任的项目(fiscal sponsor);服务对象为 10 至 19 岁青少年。
- 金额 / 规模
- 每项资助为 5 万至 10 万美元;承诺总额至少 100 万美元,分配给 10 个以上的资助项目。
- 申请窗口
- 2026-09-23 至 2026-10-16,截止时间为太平洋时间下午 5:00 / 东部时间晚上 8:00。
- 支持方向
- 学习 / 批判性思维 / 决策;可信赖的成年人;叙事表达。使用 AI 并非必要条件。
- 申请要求
- 青少年须参与项目的设计、执行和评估;须有未成年人保护措施;须有经过青少年实际测试的具体证据;须有参与的机构负责人。
- 排除情形
- 主要使营利性产品受益的项目、涉及监控用途的项目,以及一次性活动。
- 尚未解决的日期矛盾
- 该机构官网自身的资格测验表示,受资助的「学院」将于 2027 年 1 月开始;但时间表 / 常见问题页面则显示为 2027 年 4 月至 8 月。这是资助方自身材料中现存的矛盾——任何对外陈述前,须直接向 Young Futures 项目方确认,不能依赖本文摘要。
- 适配性说明
- 待核查——仅凭美国非营利组织注册身份,不足以确立一个在台湾执行的项目的申请资格;此点必须直接向对方确认,不可直接假设。
- 来源
- youngfutures.org/challenges/unwritten [9]
Spencer Foundation — Research-Practice Partnerships 已核实(条件)
- 金额 / 期限
- 最高 40 万美元(含最多 15% 间接成本);最长 3 年。
- 地域范围
- 接受美国境内及国际申请人申请。
- 核心要求
- 须已存在一个持续性的研究者—实践者伙伴关系,具有共同制定的研究议程和共享治理机制;研究生可参与,但不得担任主持人(PI)或共同主持人(Co-PI)。
- 当前窗口状态
- 预提案阶段已截止;2026-10-28 的完整提案截止日期,仅适用于已从预提案阶段获邀的申请人,并非新一轮公开征集。
- 适配性说明
- 候选研究设计 若能建立真正持续性的伙伴关系并确立主持人和主办机构,这可能是一条较长期的路径——但目前尚不适配。博士生的在学身份本身不足以确立 PI 资格,目前也没有任何已确认的合作机构。
- 来源
- spencer.org — Research-Practice Partnerships [10]
Google.org — Knowledge, Skills & Learning 待核查
- 来源要点
- 一个慈善主题页面,加上一篇关于 2025 年 3 月「AI 机会基金(AI Opportunity Fund)」受助对象的更新报道——这些证明的是过去的资助主题和过去的受助者,不代表目前存在面向 YuCROWN 的已确认、公开开放的申请渠道。
- 当前状态
- 仅作为领域监测用途;本文不主张任何截止日期、金额或申请资格。
- 来源
- google.org · blog.google — 2025 年 3 月更新 [11]
Microsoft for Nonprofits 待核查
- 来源要点
- 这是一项提供工具、额度、培训等非现金支持的计划,与现金研究资助在性质上不同。具体条款、定价与账户资格条件需直接核实,且会随时间变动。
- 适配性说明
- 候选研究设计 此类支持属于机构能力建设,不能据此证明该计划会资助 EarthLinC 的商业开发。
- 来源
- Microsoft for Nonprofits · 资格条件 [12]
非营利组织如何建立可信度
教育类非营利组织的可信度是分层积累的:合法的非营利组织身份、财务透明度、有文件记录的项目设计、真实的机构合作伙伴、参与者规模、已收集的证据、年度报告,以及——有时在更后期——独立评估或与大学的研究合作。解读 以下卡片以具名、经来源核查的例子说明这一模式;它们之间,以及与 YuCROWN 之间,并无高下排序之意。
FIRST / Brandeis 大学长期追踪研究 已核实(存在性) 待核查(细节)
- 来源要点
- 官方项目页面和大学摘要页面描述了一项为期十年、将 FIRST 参与者与同类参与者进行比较的追踪研究。
- 当前状态
- 本次查阅时,Brandeis 大学的研究详情页返回 403 错误,核查时该页无法访问;具体估计数值和研究方法未经独立核查。
- 可作为哪些表述的依据
- 这是一个累积性、长期项目证据的范例——不是关于 AI 特定效应的证明,也不能直接套用于 YuCROWN 自身的数据。
- 来源
- heller.brandeis.edu · firstinspires.org/about/impact [13]
Khan Academy — 成效研究总览 已核实(存在性)
- 来源要点
- 一个支持网站上的总览页面,列出该机构为其平台援引的成效研究。
- 需要注意区分的内容
- 「平台整体成效主张」、「组合式介入(例如本文 B 节 NBER 条目中的教练加平台)」与「AI 特定效应估计值」,三者不可混为一谈。
- 来源
- support.khanacademy.org [14]
Code.org / CodeAI — 无障碍合规报告 已核实
- 发布
- 2026 年 4 月(查阅时的官网页面;注意该机构现已更名为「CodeAI」)
- 来源要点
- 描述每年进行一次第三方无障碍评估、发布 VPAT 文件,并对照 WCAG 2.1 AA 标准,公开列出尚未达标的差距。
- 可作为哪些表述的依据
- 这是将自身局限公开披露作为建立可信度做法的一个范例——不是完全合规的主张,也不是对学习成效的主张。
- 来源
- support.code.org [15]
UNICEF — Policy Guidance on AI and Children, v3 已核实
- 发布
- 2025-12
- 来源要点
- 以权利为导向的指引,针对儿童与 AI 系统互动时的隐私、公平性、包容性、安全、残障无障碍以及健康发展等议题提出建议。
- 可作为哪些表述的依据
- 为以儿童为核心的设计原则提供有来源依据的参考点。
- 不能据此推断
- 对任何具体产品或机构(包括 EarthLinC)的认证。
- 来源
- unicef.org/innocenti [16]
合法的非营利组织身份、透明度标识、参与者规模和独立评估,代表不同种类的信任,不能相互替代。Candid / GuideStar 这类透明度标识,代表的是机构信息公开,而非教育资质认定或项目成效证明。本站点中的任何内容,都不应将 YuCROWN 描绘成提供认证证书,或已接受独立评估。
过程记录与技术标准
第2版的一处修正:第1版主张「没有任何现有标准能够呈现推理过程」,这一说法言过其实。较为准确的说法是:这些标准本质上是可扩展的框架。它们能否捕捉推理或过程,取决于输入其中的数据——而本文检视的任何标准,在默认情况下都不要求提供此类数据。
| 标准 | 范围与能力 | 尚待解决的设计问题 |
|---|---|---|
| C2PA 内容凭证(Content Credentials) 已核实 | 用密码学方法将关于内容来源与编辑历史的陈述与内容绑定。 | 能够验证这条来源记录及其绑定关系,不等于证明作品内容属实;记录是否完整取决于哪些系统提供了信息。做出某项选择背后的理由,是另一个可选的独立层级。 |
| W3C PROV-DM 已核实 | 一个可扩展的模型,涵盖实体、活动、参与主体(agent)、衍生与归属关系(W3C 建议标准,发布于 2013-04-30)。 | 提供了相应的数据结构和数据时,可对过程轨迹建模——但其本身不捕捉主观经验,也不评估证据的有效性。 |
| 1EdTech 开放徽章(Open Badges)3.0 已核实 | 可验证的成就凭证:包含颁发者、标准、证据、评估与背书等元数据字段。 | 证据字段可以链接到过程产物,但徽章本身不等于认证资质,也不能证明智力贡献。 |
| Caliper Analytics 1.2 / ADL xAPI 已核实 | 结构化的活动陈述(谁做了什么、对象是什么,可附加情境和结果)。 | 应用规范(profiles)与扩展字段可以表达更丰富的活动内容,但仅凭原始点击、时间戳和操作日志,无法确立判断力——还需配合语义编码和参与者自述。 |
来源:C2PA v2.4 规范 [17] · W3C PROV-DM [18] · Open Badges 3.0 [19] · Caliper 1.2 · xAPI data spec [20]
几类常见平台类型的比较——本文原创比较,并非来源本身的主张
常见的五类平台分别处理不同的问题。学习管理系统 / 活动日志,记录的是做了哪些行为、以什么顺序发生;作品集 / 公开作品,呈现完成的作品,日期标注较为松散;来源记录 / 成就凭证,处理来源和成就相关的元数据;研究日志 / 访谈,内容丰富但需要大量人力且难以数据导出与跨平台使用;AI 对话记录,是原始互动文本,很少与最终作品或个人后续的反思联系起来。在对任何具体产品下「存在空白」的结论之前,都需要仔细核查其版本历史、互动日志、同意记录和数据导出功能,不能默认任何一类平台是空白的。
一个开放且可检验的问题是:是否可以有一份由参与者自行掌控的记录,将以下片段串联成某个人完整的项目历程:
这是一个留给 EarthLinC 去检验的设计问题,需要与更简单的替代方案(例如日记加一个共享文件夹)进行比较——不是主张这样的记录具有独特性、已经建成,或已证实有用。
「EarthLinC 能否帮助人看清 AI 参与的学习与创作中,自己做了哪些判断和选择?记录带来的价值,是否值得投入时间与精力?」此处仅作为假设,而非结论。
把 EarthLinC 作为研究假设
EarthLinC 是 Kong Group 旗下独立的基础设施,由 YuCROWN 使用——它本身不是 YuCROWN 的某个计划。它与以人为中心的人工智能研究之间的关联性,是一项有待通过真实的教育与研究计划试点来检验的假设,目前不应对外公开主张。候选研究设计
若要使记录有意义,哪些条件必须成立
一份记录必须符合伦理、先说明用途再由参与者知情选择是否记录,能够导出并在其他环境中继续使用,并且对其所关乎的当事人真正有用——意思是当事人能够看见、理解并掌控关于自己过程的记录,记录能够随本人移动,而不是被锁在单一平台中。它也应该接受这样的检验:「这是否真的比一本日记加一个共享文件夹更好?」——不能预设它理所当然更优越。
不应被记录的内容
任何可能损害隐私的内容,对未成年人尤其如此;任何未经清楚、适合年龄、可理解的说明而收集的内容;任何会将一个本该出于选择的学习过程,变成持续监控而非可供查阅、自愿加入之记录的做法。
技术准备情况——诚实说明
| 功能 | 当前状态 |
|---|---|
| 可编写的站点以及站点间相互链接 | 既有项目来源描述此功能已存在。 |
| 自愿的「铸造(mint)」/ 加冕(crown)操作 | 既有项目来源描述此功能已存在。 |
| 完整的版本历史记录 | 待核查 |
| 包含模型元数据的完整 AI 互动日志 | 待核查 |
| 权限分级 / 基于角色的访问控制 | 待核查 |
| 数据导出、同意记录账本、撤回与删除机制 | 待核查——在核查前不应假设删除功能已经存在 |
目前已知,现有的页面编辑工具(refine)在替换页面内容时,不保证留下版本历史轨迹。链上指针或铸造操作所记录的,只是某个操作曾经发生过——它本身不足以确立法律所有权、学习是否发生、人类贡献的完整范围,或知情的研究同意。
一份候选的、尚未建成的研究记录
候选研究设计 仅适用于未来的试点项目:使用化名或编码的项目编号(不代表完全匿名);一项可选的原始观察或提问;经挑选的草稿;一则说明关键选择的简短决策笔记;一项可选的说明(说明 AI 参与了哪些部分);参与者的反思;导师的回应;以及由参与者自行选择的分享权限。任何时候都只应收集特定研究问题所需的最少数据。参与者必须能够拒绝参与研究,同时仍可完整地进行创作、使用并贡献内容。可识别身份或敏感的研究数据,应保留在公开站点及任何不可篡改的账本之外;数据的保存期限与撤回方式,应在任何数据收集开始之前就加以说明,而不是假设这份参考资料已经解决了这个问题。
与 YuCROWN 各计划的关系
这是一份参考性的对应关系梳理,不是对任何计划的重新设计,也不是对其他站点的编辑修改。下表每一行,是一项外部研究信号搭配一项可能的、尚未定案的内部参考意涵;其后的说明将证据对应到 YuCROWN 实际的各个计划。
| 外部研究信号 | YuCROWN 可能的参考意涵 |
|---|---|
| 人的自主性在各框架中均居核心地位,并与 AI 素养相互交叉 | 教育与研究计划(Study)应衡量判断与决策过程,而不只是产出质量。 |
| 关于认知外包的范围有限的证据(B 节) | 项目设计应让推理与修改的过程可见,而不只是呈现完成的作品。 |
| 来源记录标准可扩展,但默认不自动要求记录人的理由与主观经验(E 节) | Earth Library / EarthLinC 可以将一套更强、以同意为基础的过程记录作为研究问题加以检验。 |
| 资助领域中的研究—实践伙伴关系模式(C 节) | 学校 / 社区项目有可能随时间被共同设计为真正的研究情境,而非单纯的执行场地——但这取决于能否建立真正持续性的伙伴关系。 |
| 标准与指引中明确提及无障碍(D、E 节) | Cali 的框架应从一开始就将无障碍视为核心,参见 J 节。 |
本参考资料采用目前明确的项目定义:Earth Library 是一个公共环境,其中独立创作的作品与人的背景及经历在彼此关联中呈现;Conversations 是一个以年轻人提问为核心、经验在世代之间传递的空间。这些定义指导本站点的用语。其他来源仍保留不同的表述,差异需由负责人审阅确认,本文不在此悄悄融合或修改:
- YuCROWN OS 站点,仍将 Earth Library 描述为「活的收藏 / 经策划的公开计划」,并将 Study 描述为较新的研究功能——OS: Status & Infrastructure。
- 公开的组织站点,以较为狭义、侧重专业身份的方式定义 Study——Connecting Realms of World Narrative。
- Cali 的研究笔记简报(2026-10-01),在 Study 之下将该计划命名为「以人为中心的人工智能教育与研究计划(Human-Centered AI Education and Research Program)」。本站点不应单方面将此计划名称更改为「学习与研究」或以其他方式重新定义——For Cali — Research Notes。
本参考资料不对 YuCROWN OS 或任何公开站点作出任何变更。这些定义如何对齐,应留待未来的人工审阅来决定。
将证据对应至各计划
Fellow(A Colour Only You Can Leave):原创性的创作、个人表达与判断。从研究视角来看,可以观察想法如何演变、选择是否能被清楚解释——而不只是统计产出了多少站点。
Conversations(C — Conversations):以年轻人提问为起点,经验在世代之间传递,直到部分内容在传递中消散(提问 ASK → 聆听 LISTEN → 经历 EXPERIENCE → 带入实践 CARRY → 留存 REMAIN)。从研究视角可以检视:问题如何演变、较年轻的参与者将什么带入日后的实践,以及有经验的参与者觉得有哪些内容被遗漏或被错误呈现——而不是泛泛的对话,也不是由 AI 生成的「经验」。Cali 现有笔记中保留了早期草稿中较为笼统的 Conversations 用语;这类用语不应被直接引入本站点的定义。
Field:具体的实践情境。从研究视角应检视已经存在的真实观察和数据,不应虚构尚未确认的合作伙伴或地点。
Earth Library(Earth Library):一个公共空间,其中独立创作的作品与人的背景及经历在彼此关联中呈现。在这里,自主选择的公开呈现是一种主动行为,不是自动产生的研究数据集。
Study:依据 Cali 的研究笔记简报,这是一项「以人为中心的人工智能教育与研究计划」,以参与者的兴趣、生活经历与提问为核心,与教师、Fellow 和导师合作。正式的研究问题和方法仍由 Cali 本人设计——下方 I 节提供的是候选方案,不是最终决定。与 KAIST(韩国科学技术院)相关的个人经历是个人学术资历,不是机构合作关系;学校提案(把日子織成星空)目前仍只是提案,不是已确认的入学、合作关系或资助承诺。
Conversations 的类比参考(仅供情境参考——并非背书或重新定义):UNESCO《活态遗产与教育》官网页面摘录,描述世代间传递是一种互动式的非正式学习;本次查阅时,该页面完整内容部分受阻,因此只在「来源摘录」层级进行了核实。ich.unesco.org [21]
如何用于下一步工作
本站点支持的核心立场是:在一个由 AI 深度介入的世界中,人的判断、表达、作者身份与创作贡献,以及人与人之间的关系,才是研究与实践的核心。AI 是工具、协作媒介和研究情境;围绕人的那些问题,才是真正值得探究的。
| 工作领域 | 现有依据能支持什么 | 还需补充什么 | 负责人或审阅者 |
|---|---|---|---|
| 叙事表述 | 站得住脚的用语:人的自主性、判断、作者身份与创作贡献、过程可见性(A–B 节)。 | 使用已有来源依据的用语无需额外条件;避免主张与机构存在关联。 | 责任归属待指派——目前并非已任命的角色。 |
| Study | 候选研究问题与方法清单(I 节)。 | 在任何指标定案之前,先取得试点可行性数据。 | Cali(已确认的负责人) |
| 资助 | 按各资助方列出的已核实条件与待解问题(C 节)。 | 对外引用日期 / 金额前,须直接向各资助方核查最新信息。 | 责任归属待指派——目前并非已任命的角色。 |
| 项目 / Field | 对应关系梳理(G 节)以及需留意的人的过程链(E 节)。 | 任何过程记录都需有一个最小化、符合同意原则的版本,并与真实参与者进行测试。 | 责任归属待指派——目前并非已任命的角色;Cali 参与研究设计。 |
| EarthLinC / 技术协议 | 一份可检验的假设清单,以及对当前技术准备情况的诚实说明(F 节)。 | 一次真正的技术能力核查,而非假设功能已经存在。 | Kong Group(次要于试点需求——责任归属待确认) |
| 网站对外内容 | 只有标注为「已核实」且附有来源的主张,才可公开呈现。 | 须在发布当时重新核查,而非仅依赖撰写当时的核实状态。 | 发布前须经人工审阅 |
| 未来研究 | 一份标注日期、列出具名待解问题的起始地图(L 节)。 | 须就 OECD、Stanford HAI、NBER 完整文本、FIRST 完整报告以及各资助方官网现有页面重新回到来源核查。 | 下一位修订本站点的人 |
候选研究问题与方法
以下三个候选问题供 Cali 审阅——不是已承诺执行的研究,也不意味着其中任何一项目前正在进行。候选研究设计
研究问题一——能否在新任务中独立运用所学
在何种导师指导和 AI 支架式协助(scaffolding)条件下,参与者日后能够独立解释并在新情境中运用某一关键决策?候选方法:比较最初的任务与之后类似的任务;收集参与者简短的口头或书面说明,以及他们自行挑选的草稿;将「有协助下的质量评分」与「独立理解程度的评分」分开记录。
研究问题二——个人表达与作品多样性
AI 的协助如何影响一位参与者在连续数个项目中的个人表达风格(voice,指一个人区别于他人的表达方式)与作品多样性?候选方法:收集草稿和简短的决策笔记;让参与者用自己的话描述「风格」是什么;采用由多位评分者参与、搭配共同设计评分标准的脉络化审阅方式;不应使用 AI 作者身份检测工具作为证据,因为这类工具尚未针对此用途经过验证。
研究问题三——对话中的经验如何进入后续实践
在 Conversations 中提问的年轻人,会将什么带入日后的选择或实践?候选方法:追踪一个问题在对话过程中如何演变;以受访者认可的方式记录这次交流;在参与者自行选择的时间点进行后续反思的跟进——而不是将单一总结式的记录,当作对年长者完整经验的定论。
方法路径(大致按伦理与实务难易程度排列)
首先从可行性探索和参与式研究开始,了解什么在这个情境中是可行的;再进行混合方法或质性个案研究,对同一人进行多次观察;只有在确实可行且符合伦理时,才加入比较组;只有在问题和资源明确支持的情况下,才在更后期考虑随机对照设计。任何研究设计都应事先明确研究问题,并处理以下内容:抽样与招募方式;多语言翻译上的选择;编码一致性;缺失数据与中途退出;混淆变量(教师、时间、先前技能水平);研究者自身的立场与利益冲突;反例或不支持假设的案例;以及请参与者核对研究者的理解(member-checking)。一次小规模试点可以展示可行性和丰富的过程记录,但不应被汇报为具有普遍性的因果证明。本文不设定任何样本量、统计功效(power)计算或时间表;这些都必须与 Cali 共同设计,不能预先假定。
研究概念对照——未经验证的候选项,仅供讨论
| 研究概念 | 可尝试观察的证据 | 不能用什么替代 |
|---|---|---|
| 自主性(agency) | 清楚表达的选择与说明的理由 | 单纯拒绝 AI 建议的次数多少 |
| 学习(learning) | 在后续任务中独立解释或迁移运用的能力 | 单一有协助产出的润色程度或质量 |
| 作者身份与创作贡献(authorship) | 本人自述、草稿历程、明确表达的署名 | 关于所有权的法律裁定 |
| 个人表达风格 / 多样性 | 参与者在情境中自身的判断 | 单一评分者或检测工具给出的新颖性评分 |
| 人际关系 | 有意义的交流与反思内容 | 建立联系或链接的数量 |
| 可及性(access) | 在获得所需无障碍支持下参与的能力 | 单纯建立或激活账户 |
| 负担 / 身心健康 | 参与者对投入时间和能否中止的反馈 | 临床或医学评估 |
一位学习者录下一段环境中的声音,将自己的观察与 AI 生成的描述进行比较,解释自己为何保留或拒绝了其中部分内容,向有当地经验的人请教自己听到的内容,创作出一段短作品,并自行选择是否以及如何分享。以上每个步骤,都对应上方对照表中的不同研究概念——这个情境只是为了说明哪些证据可以回答哪些问题,不描述任何已经发生的事情。
参与、无障碍与权利
一项原创的候选设计,参考了 UNICEF 的指引[16]——不是对目前已实施内容的描述。候选研究设计
区分不同种类的同意
一般性地参与某个项目、选择加入研究、授权被记录,以及选择公开分享,应被视为四项各自独立的决定。拒绝参与研究,绝不应导致失去一般性的支持,或无法参与活动本身。
符合年龄的流程
须提供清楚、适合年龄的说明,并在需要时取得监护人同意;适当的伦理或机构审核,应通过实际的程序来确定,不能假设或对外声称已经取得。数据收集应力求最少,以基于角色的权限私密存储,须有明确的保存期限和撤回流程,须能供参与者本人查阅,且不应要求必须公开发布。
从设计之初就纳入无障碍
音频和文字替代方案、字幕与逐字稿、键盘导航、低带宽或离线草稿功能、参与者偏好的语言,以及有弹性的进度安排,都应从一开始就内建于设计中——而不是等到试点暴露出差距之后才补充。青少年共同设计,应意味着对研究问题、解释方式和呈现方式拥有实质的主导权,而不只是在决策已经作出后收集反馈。居住在乡村地区,绝不应被当作能力较低的代理指标。
成本与角色须保持分离
由非营利资助支持的项目和研究成本,应与 Kong Group 对 EarthLinC 的商业开发保持分离。任何未来的服务或费用安排,都需要有其自身实际、公开说明的条款——不能仅因出现在本参考资料中,就假设为免费、享有折扣或已经获得批准。
可以怎样对外表述
本站点所引用的来源实际上能支持哪些表述,哪些内容仍未获证实。
| 已有依据 | 仍需验证的设想 | 目前不能声称 |
|---|---|---|
| HCAI 与 AI 素养和负责任的 AI 相互交叉,而非取代两者(A 节)。 | Study 可以检验一份可见的决策与修改轨迹,是否能提升可衡量的独立迁移能力(I 节)。 | 不能声称 YuCROWN 的任何计划已改善学习、自主性或创造力。 |
| 具名的范围有限的研究显示,AI 协助的效应因情境而异,结果好坏参半(B 节)。 | 原则上,EarthLinC 有可能记录比一般活动日志更完整的人的过程链(E–F 节)。 | 不能声称 YuCROWN 的做法具有独特性、更为优越或已经经过验证。 |
| Young Futures 和 Spencer 均存在具体、标注日期的资助条件,并附有具名的待解问题(C 节)。 | 若能建立起持续性的伙伴关系,研究—实践伙伴关系这条路径有可能开启(C 节)。 | 不能声称任何资助方已对 YuCROWN 表示背书、审核或承诺。 |
| 具名的各项标准(C2PA、PROV、Open Badges、Caliper / xAPI)具可扩展性,部分具备记录过程的能力(E 节)。 | 一份以同意为基础、由参与者掌控的过程记录,是一个可检验的设计目标(F、J 节)。 | 不能声称已有确认的学校合作关系、伦理审核通过,或已完成的研究。 |
申请资助前需要准备的材料
一份有来源支持的问题陈述;一项范围明确、已在运行的项目活动;有文件记录的青少年共同设计证据;已实际测试的作品以及经许可收集的参与者反馈;若有合作伙伴意向书,须是真正已确认的;明确的角色与成本责任划分;一份衡量计划;一份治理说明;以及该特定资助方目前的申请资格、所处阶段与截止日期。内部目前未知或尚未核实的数据,应诚实列为「未知」,而非加以估算。
拟议的变化路径(Theory of Change)——以假设而非已证实结果呈现
所谓「变化路径」,是指描述一项活动可能如何产生改变的逻辑链条。候选路径为:兴趣或提问 → 获得支持的实践与真实的选择 → 可被解释的作品与反思 → 潜在的能力或联系 → 证据审阅。这条链中的每一个箭头,都是留给 Study 去检验的开放假设,不是主张其中任何阶段已被证实。
来源与后续核查
这是对精选原始来源进行的有范围综述,并非系统性或穷尽性的文献综述。除另有注明外,以下所有查阅日期均为 2026-10-03。
下一次修订的待办优先事项
在作出任何申请资格或申请决定之前,先审阅 Young Futures 的完整征求建议书(RFP);直接向资助方项目方确认在台湾执行项目的申请资格,并解决「学院」开始时间究竟是 2027 年 1 月还是 2027 年 4 月至 8 月的矛盾——不能依赖本参考资料的摘要。在引用任何效应量之前,先取得并审阅 NBER w35620 的完整文本。取得并审阅 FIRST / Brandeis 大学的完整研究方法与发布日期。在任何数据收集之前,与 Cali 共同设计实际的研究工具,并寻求适当的伦理审核。对 EarthLinC 进行一次真正的技术能力核查,而不是依赖本参考资料中诚实标注的「待核查」占位说明。通过人工审阅——而非由本站点单方面决定——使 Earth Library、Conversations 与 Study 的规范定义,在 YuCROWN OS 站点、公开的组织站点与 Cali 的笔记之间重新对齐。
使用前重新核对
有时效性的内容(资助申请窗口、计划名称、查阅时的官网页面内容)必须在使用当时重新核查,不能假设本参考资料的内容仍然是最新的。原始研究条目应在本站点进行实质性修订时重新查阅,没有固定的更新排程。
- 扩充了定义、证据、资助和技术标准等方面的来源核实工作;新增包含 21 项条目、附查阅日期和核实等级的文献目录。
- 新增有范围、针对特定来源的实证证据(B 节),取代原先的笼统陈述。
- 修正了「HCAI 取代 AI 素养 / 负责任的 AI」这一言过其实的说法,以及「没有任何标准能够呈现推理过程」这一言过其实的说法。
- 新增已核实的资助条件,包括 Young Futures 自身材料中一项尚未解决的日期矛盾。
- 新增候选研究问题与方法(I 节)、参与与无障碍设计(J 节)、对外表述盘点(K 节),以及本文献目录(L 节)。
- 新增与 YuCROWN 各计划的对应关系梳理,并附明确、尚未解决的来源对齐说明(G 节)——未对任何其他站点进行编辑修改。
- 已加入封面插画。
本次修订按中文研究读者的阅读习惯重新改写了简介及 A、B、C 节,统一核心术语(以人为中心的人工智能、自主性、判断、作者身份与创作贡献、所有权、来源与流转记录、过程记录、教育与研究计划)并理顺段落逻辑,同时保留所有数字、来源链接、日期、局限说明与原始信息。D 至 L 节保持不变。这是语言与表达层面的修订,未新增或更改任何研究内容、来源、数据或主张。
研究精选 / Research Watch
一个独立的持续性栏目,服务于 YuCROWN Study(Human-Centered AI Learning & Research)。本节为独立栏目,不属于 A–L 章节的研究内容。
栏目设置 已收录五期
- 栏目设定日期
- 2026-10-18
- 已收录
- 2026-10-18(首期)、2026-10-23(第二期)、2026-10-30(第三期)、2026-11-06(第四期)、2026-11-13(第五期)
- 下次例行精选
- 2026-11-20;之后每周五上午(Asia/Shanghai)。
- 当前状态
- 已收录:2026-10-18、2026-10-23、2026-10-30、2026-11-06、2026-11-13;下次例行精选:2026-11-20。
- 重点
- 青年自主表达与判断;人类创作与个人经验的作者身份、控制权及所有权;人类与 AI 的协作关系。
每期收录标准与记录项目
每期精选 3–5 项新论文、研究报告或高质量学术分析,宁缺毋滥。每项记录:原文标题;作者/机构;发表日期;分类(同行评审/预印本/报告/观点);原始链接及 DOI(如有);核心发现;方法与样本;证据强度及局限;对 Earth Library 或 Study 的具体启发;以及青年可独立探索的问题和起步方式。
须区分原研究结论与实践推论。若只读到摘要,或研究不含青年样本,须明确标注。
去重与历史
每期精选将在此按日期保留。去重时结合本站既有来源目录(L 节),以 DOI、规范化标题以及预印本与正式版本的关系为依据;仅在出现实质性新结果或发表状态变化时才重新收录,并说明新增之处。既有来源的日期与核实状态保持原样。
2026-11-13 · 第五期
检索重点:2026-11-07 至 11-13 新发表或实质更新的内容。已读取本站,核对 L 节 21 项基础目录、M 节前四期与 15 项去重索引;以 DOI、规范化标题和版本关系去重,本期 3 项均未收录。2 项已阅读全文,1 项仅摘要;不以搜索收录/抓取日期代替原文发表日期。这里不同论文的 agency 定义不同,不能直接合并为同一指标。作者感、心理所有权、署名责任、发布控制与法律权利分别处理;本期不作版权法律判断。所有实践启发和青年问题均为 YuCROWN 推论/候选探索,不是已验证的项目效果。
1. Generative AI use and scientific literacy among 15-year-olds in Beijing, Shanghai, Jiangsu, and Zhejiang, China: the mediating role of academic help-seeking 已核实(全文)
- 作者/机构
- Bing Wu、Xianpeng Jiao、Changle Yang、Jie Wang、Qingshuang Dong;Qufu Normal University, School of Communication。
- 日期/类型
- 2026-11-09,Frontiers in Psychology,已同行评审 Original Research,全文已读。
- 来源
- frontiersin.org — 全文
- DOI
- 10.3389/fpsyg.2026.2013021
- 查阅日期
- 2026-11-13
- 方法/样本
- PISA 2025 中国四省市数据二次分析,12,191 名 15 岁学生、374 所学校;加权两层模型、10 个能力合理值,另以学校固定效应及 BRR 权重复核。AI 频率、求助与元认知为自报,科学素养为测验。
- 发现
- AI 使用与科学素养的双变量相关为零;控制模型中有负向直接关联,以及经求助等变量的正向间接关联。经求助的间接关联 β=.013。这里的求助指向同学/教师的一般倾向,不是向聊天机器人求助,也不能确定先后顺序。
- 证据
- 中(关联),低(因果)。直接包含青年、设计权重处理较仔细;但为横断面、无先前成绩、未预注册,反向模型同样适配。小效应、缺失样本较弱、四省市高成就情境限制外推;不能说 AI 提升或损害科学素养,也不能说 AI 促使学生向人求助。
- Study 启发
- YuCROWN 推论 / 候选探索 分开观察“用 AI 多少”“用来做什么”“之后向谁求助”。小尝试:沿“提出问题—观察记录—整理发现—表达—带给他人—听见回应”,保存一个由 AI 讨论产生的疑问,请一位同伴/老师回应,再自行修订解释;研究未验证此流程。
- 青年问题/起步
- 候选探索 “AI 回答后,我还有什么需要问一个真实的人?”以小船/动物观察为题,先写自己的解释,标出一个未解问题,核查一条原始来源并听一次回应。可私下记录,不据个人经历推断普遍因果。
- 所有权边界
- 该研究不测作者感或法律所有权。
2. Perceived Agency During Agent–Avatar Control Transitions in Virtual Reality 已核实(全文) 预印本 · 同行评审未核实
- 作者/机构
- Jingyi Zhang、Anthony Steed;University College London。
- 日期/类型
- 2026-11-09,arXiv:2611.12529v1 预印本,同行评审/正式会议发表未核实;全文已读。
- 全文
- arxiv.org/html/2611.12529v1
- 元数据
- arxiv.org/abs/2611.12529
- 仓储 DOI
- 10.48550/arXiv.2611.12529(arXiv 标注 DataCite 注册待完成,非正式会议 DOI)
- 查阅日期
- 2026-11-13
- 方法/样本
- 24 名成年人(18 岁以上,年龄众数 25–29),75 分钟被试内 VR 实验,四条件顺序用拉丁方平衡:始终预录、预录转真人、真人转预录、始终真人;固定五问访谈,不许追问。所谓“计算机控制”实际是实验员触发预录真人言语/动作(Wizard-of-Oz),不是生成式 AI。数据包括问卷、回看录像的连续滑条和访谈。
- 发现
- 控制转换后未发现感知控制者有显著更新;判断较受最初披露与初始行为共同影响,不能单独归因于标签。后段真人控制的情境更受喜爱。这里的 perceived agency 指相信虚拟角色由真人还是程序控制,不是参与者自己的自主性。
- 证据
- 低—中,限于此短时实验。被试内设计和平衡顺序有价值;n=24、固定问答、回顾测量、主观结果、实时与预录行为差异限制解释。无显著更新不证明绝不更新;不能外推到真实生成模型、青年、长期关系或创作所有权。
- Earth Library / Study 启发
- YuCROWN 推论 / 候选探索 展示他人经验或 AI 辅助回应时,作者、原始经验提供者、AI 改写者与当前回应者应可区分,不能靠拟人表现让读者去猜。小尝试:为一个经本人同意的片段加简短贡献说明,让同伴解释“哪些话来自谁”;不做欺骗实验,也不复制真人声音/形象。
- 青年问题/起步
- 候选探索 “我觉得有人在回应,是因为内容、表情,还是来源说明?”比较本人作品的原文和明确标注的 AI 辅助改写,记下判断依据,再问作者是否准确。不将“感觉像真人”当作真实经验或授权的证明。
3. AI and the responsibility-bearing status of authorship 仅摘要 · 全文待核查
- 作者/机构
- Caterina Moruzzi;University of Edinburgh,Edinburgh College of Art / Design Informatics。
- 日期/类型
- 机构记录 2026-11-12 Accepted/In press;Philosophy & Technology,同行评审后接收的概念/哲学分析,非实证研究。不是已核实的正式卷期出版。
- 原始机构来源
- research.ed.ac.uk — 机构记录
- DOI
- 机构记录未列可核实的文章 DOI,不虚构。
- 查阅日期
- 2026-11-13(仅摘要)
- 阅读边界
- 仅核验机构公开摘要与元数据;接受稿被标为 Embargoed,未阅读全文。不能核验完整法域范围、案例选择、论证反例和边界。
- 核心主张(只据摘要)
- 作者将版权中隐含的作者身份解释为承担责任的地位,条件包括意向行动、引导性控制和关系性表达(ACE);据此讨论人类 AI 辅助作者身份和部分生成输出的作者缺口。这是作者的理论重构,不是法院裁决或新的法律规则,也不等于主观上的“这是我的”。
- 方法/对象
- 哲学/概念论证;无实证参与者或干预,摘要未交代完整法律材料与分析程序。没有青年数据。
- 证据
- 实证强度不适用;论证可信度目前无法充分评定(仅摘要),同行评审接收不等于实证验证或适用于各法域。不得据此认定任何 Earth Library 作品的法律所有权。
- Earth Library / Study 启发
- YuCROWN 推论 / 候选探索 把意图、实际选择、表达来源与发布责任分别说清。小尝试:创作者可自愿写三句“我想表达什么/我作了什么决定/我愿意为哪部分负责”,分享许可另行表达;这不是法律测试,也不能由此生成版权结论。
- 青年问题/起步
- 候选探索 “我愿意署名、我觉得像自己、我愿意负责,会在何时分开?”用一段非敏感小作品分别回答三问,可拒绝任何标签或不公开。
求助于人不等于放弃判断;感觉真人在场不证明真实的人正在回应;愿意署名、主观作者感与法律权利也不互相替代。Study(Human-Centered AI Learning & Research)可从一个自己的问题出发,保留一次取舍、一个核实来源、一段独立解释和一个真实回应。Earth Library 呈现自愿分享的作品,不能把作品或私人过程默认转为研究数据。
本期 DOI / 标题去重清单
- 10.3389/fpsyg.2026.2013021 — Generative AI use and scientific literacy among 15-year-olds in Beijing, Shanghai, Jiangsu, and Zhejiang, China: the mediating role of academic help-seeking — 正式全文 2026-11-09,核查 11-13,新。
- 10.48550/arXiv.2611.12529 — Perceived Agency During Agent–Avatar Control Transitions in Virtual Reality — v1,2026-11-09;同一研究的后续会议稿须合并版本。
- 无可核实 DOI — AI and the responsibility-bearing status of authorship — Moruzzi,2026-11-12 accepted/in press,仅摘要;以规范化标题+作者+机构 URL 去重,正式全文出现时作版本更新。
2026-11-06 · 第四期
检索重点为 2026-10-31 至 11-06;已读取当前站全文、L 节 21 项基础目录、M 节前三期和 12 项去重索引,按 DOI、规范化标题及版本关系去重。以下 3 项此前未收录,另 1 项为同一研究的正式发表更新。全部已阅读全文。新资料属于预印本/开放会议稿,不因进入 arXiv 或会议节目就自动当作已核实同行评审。这里的 agency、作者感和控制感不是法律所有权;本期不判断版权归属。青年探索及 Earth Library / Study 做法均为 YuCROWN 推论,未经这些论文验证。
1. Who Is Doing the Thinking? Characterising Human Agency in First-Year Students’ AI Interactions 已核实(全文) 预印本 · 同行评审未核实
- 作者/机构
- Ali Shakiba、Thomas Chaffey;The University of Sydney。
- 日期/分类
- 2026-11-03,arXiv:2611.04783v1;预印本,未核实同行评审。正文仍有会议模板占位符,不能把 2018 模板日期或占位 DOI 当发表信息。
- 原始全文
- arxiv.org/html/2611.04783v1
- 元数据
- arxiv.org/abs/2611.04783
- 仓储 DOI
- 10.48550/arXiv.2611.04783(arXiv 标注 DataCite 注册待完成,非期刊 DOI)
- 查阅日期
- 2026-11-06
- 发现/方法
- 一门允许 AI 的大一软件工程课程,105 名学生自报日志的 962 条提示,覆盖 10 周;27 名持续记录者进入纵向子样本。HADT 六类描述提示中可见的人的工程贡献:84.2% 超出完全委托;类型随项目阶段变化。它不直接测学习,也不是分数越高越好。
- 证据强度
- 低—中(描述性)。有双人编码抽查和学生内聚类复核;单课、自选记录、后期流失、教师兼编码者,忽略对话历史/附件,且无多重检验校正。不能证明阶段或 AI 造成 agency 变化;大学生年龄未明确报告,不能当未成年人证据。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 Study 在“提出问题—观察—整理—表达”各选一段本人自愿保留的决策记录,写“我先做了什么判断”,不把提示长短或成品复杂度作学习指标。
- 青年独立探索
- 候选探索 “我写进提示的哪些内容来自自己的观察?”起步:选 3 次互动,标出观察、判断、委托各一句,关掉 AI 再解释一次。只做个人探索,不用 HADT 给人排名。
2. Measuring the collaboration, not the agent 已核实(全文) 预印本 · 同行评审未核实
- 全文扩展标题
- Measuring the Collaboration, Not the Agent: An Evaluation Framework for Human–AI High-Dimensional Visual Analytics
- 作者/机构
- Xinxiao Li(Shonan Institute of Technology)、Eisei Nakahara(Nihon University)。
- 日期/分类
- 2026-11-04,arXiv:2611.06487v1;预印本/评价框架与模拟研究。IEEE VIS 2026 VISxGenAI 官方节目列入 11/09 工作坊;节目本身不足以核实正式同行评审或期刊发表。
- 原始全文
- arxiv.org/html/2611.06487v1
- 元数据
- arxiv.org/abs/2611.06487
- 节目
- rio.ieeevis.org — VIS 2026 节目页
- 仓储 DOI
- 10.48550/arXiv.2611.06487(注册待完成)
- 查阅日期
- 2026-11-06
- 发现/方法
- 提出互补性、人的干预成本、适当依赖、跨会话保留人的判断、agency 五轴。用 SECOM 的 1,567 条半导体生产记录及模拟分析师校准前四轴;没有真人。模拟证明指标可算、会随设定变化;“知识保留”指代理保留人的反馈,不是学生记忆或学习。
- 证据强度
- 对真人效果不适用;作为模拟指标校准为低—中。所谓真值为监督分析代理,结果对权重和模拟策略敏感,第五轴未检验;不能推论青年、学习、真实协作效益或所有权。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 Study 比较自己完成、AI 独做、协作三份小解释,既看质量也记修改负担,避免把 AI 产出好直接视为协作好。不是复制其工业评分函数。
- 青年独立探索
- 候选探索 “多花一次人工判断,到底改善了什么?”起步:只记录三次接受/拒绝/修改的理由,请同伴盲看哪处更清楚;本人可选择不分享过程。
3. CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances 已核实(全文) 预印本/已接收会议稿 · 评审未核实
- 作者/机构
- Torin Hopkins、Shih-Yu Ma、Zander Trafton-Hancock、Ellen Yi-Luen Do、Luca Turchet;SolJAMM/University of Colorado Boulder、University of Trento。
- 日期/分类
- 2026-11-02,arXiv:2611.03389v1 开放稿;作者官网标 IEEE IS2 2026 Accepted。已核实作者的会议接收声明,未独立核实评审程序或正式会议 DOI;保守列预印本/已接收会议开放稿。
- 原始全文
- arxiv.org/html/2611.03389v1
- 元数据
- arxiv.org/abs/2611.03389
- 作者来源
- torinhopkins.com/publications/crowdforge
- 仓储 DOI
- 10.48550/arXiv.2611.03389(注册待完成)
- 查阅日期
- 2026-11-06
- 发现/方法
- 两场现场音乐演出,19 名观众(平均 28.89 岁)、2 名表演者;音频/视觉/两者三条件。问卷 14 人、行为记录 17 人、访谈 6 人。总体 agency 模式差异不显著(p=.721);定性解释强调能辨认自己选择的后果。作者“可观察性驱动 agency”应视为探索解释,不是已验证因果机制。
- 证据强度
- 低。真实混合方法场景有价值,但样本小、组别不均、条件/表演混杂、多次探索检验和自报;年龄范围未明,不能声称未成年人样本或青年适用。参与、主观控制、人与人的连接是不同结果。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 Earth Library / Study 让作者自愿展示一次“我的选择→可辨认的变化”,而非增加更多按钮;同时询问与他人的连接是否增强。
- 青年独立探索
- 候选探索 “我能看见选择的影响,就更觉得这是我的表达吗?”起步:同一小作品比较两次修改,记录可见影响、作者感、他人回应各一句;不推断法律所有权。
Generative AI configurations across creative phases: a 2 × 2 experiment on creative performance and immediate knowledge in art and design education
Yiming Wei、Sijun Li(Xi’an Academy of Fine Arts);DOI 10.3389/fpsyg.2026.1971021。
第二期 2026-10-23 仅核验摘要;现已于 2026-11-04 在 Frontiers in Psychology 正式发表(已同行评审 Original Research),2026-11-06 阅读全文:frontiersin.org — 全文。
新增核验:120 名 18 岁以上艺术/设计学生随机分为四组,每组 30 人;唐三彩骆驼数字配色重构;三名盲评专家和即时知识测试。证据中等,限于本任务的固定配置比较;配置捆绑模态、互动方式和界面,不能把差异归因于纯文字或图像。即时测验事前未经独立验证,事后补做验证不等于事前验证;无长期迁移。
对第二期的更正:第二期摘要解读中“未经家族错误率校正后不再显著”有歧义;准确表述为:图像配置在收敛阶段的美学优势,经 Holm 多重比较校正后不显著。第二期原文作为历史保留,以本注记明确更正。Text→Image 没有总体创造表现优势;即时知识表现较高不证明长期学习。本次没有新增实验或样本,不重复计数。
YuCROWN 推论/尝试:把作品评分与关掉 AI 后的解释分别记录,不预设通用最佳顺序。
青年问题/起步:“先文字后图像更帮我理解,还是只帮我完成?”自选一小任务,结束后 60 秒口述理由;这是探索假设,不是未成年人已验证方法。
对 Study(Human-Centered AI Learning & Research)而言,可见过程、主观控制、独立能力、协作质量和人与人的回应必须分别看;日志不是思想本身,模拟不是人类效果,接收不是高证据。可沿 Study 既有过程,自愿留一项问题、一次取舍、一段独立解释和一个真实回应,是否公开由作者决定。
本期去重清单
- 10.48550/arXiv.2611.04783 — Who Is Doing the Thinking? Characterising Human Agency in First-Year Students’ AI Interactions — v1,2026-11-03,新。
- 10.48550/arXiv.2611.06487 — Measuring the collaboration, not the agent(全文/工作坊扩展标题为同一研究)— v1,2026-11-04,新。
- 10.48550/arXiv.2611.03389 — CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances — v1,2026-11-02,作者会议稿为同一研究。
- 10.3389/fpsyg.2026.1971021 — Generative AI configurations across creative phases: a 2 × 2 experiment on creative performance and immediate knowledge in art and design education — 第二期同一研究,11/04 正式发表,11/06 全文核验。
2026-10-30 · 第三期
本期主题:把“会检查答案”“会质疑系统”“把什么认知功能交给 AI”“是否持续监控自己的思考”“能否用地方经验改写通用建议”分开观察。本期检索并核验 2026-10-24 至 2026-10-30 新发表或发生实质版本变化的内容;以 DOI、规范化标题和预印本/正式发表关系,与本站 A–L、2026-10-18 首期和 2026-10-23 第二期逐项去重。以下 4 项为不重复的新研究,均已读取全文、均为已同行评审研究;只有第 1 项直接包含未成年人。第 2–4 项向青年情境外推有限。以下“作者感/控制感/心理所有权”是主观经验,不等于版权或其他法律所有权;本期研究均不能据此判断法律权利。每项“对 Earth Library / Study 的启发”和“青年探索”为 YuCROWN 推论,不是原研究结论。
1. Student epistemic agency in AI-mediated practices 已核实(全文)
- 作者/机构
- Nina Bergdahl;Halmstad University,瑞典。
- 类型与状态
- 已同行评审 Research article,Smart Learning Environments;2026-10-24 在线发表,Version of Record 2026-10-26;全文已核验。
- 原始来源
- link.springer.com/article/10.1186/s40561-026-00464-y
- DOI
- 10.1186/s40561-026-00464-y
- 查阅日期
- 2026-10-30
- 核心发现
- 学生在“判断 AI 输出”上的认识主体性强于在“质疑 AI 系统”上的主体性。47 人中 35 人输出导向得分高于系统导向,4 人相反,8 人相同;精确符号检验 p<.001。79% 学生在九个情境的反应跨度至少 3 档,说明主体性随任务而变化。学生较能处理来源评价、责任和治理,却较少质疑数据隐私与算法操纵:66% 在隐私情境选择最低主体性反应,47% 在算法影响情境选择最低反应。一位学生把“改动 AI 答案”作为称游戏为自己作品的条件;这是个人作者感线索,不是法律权利判断。
- 方法与样本
- 瑞典一所义务教育学校的探索性混合方法个案研究;七年级 47 名学生和 1 名教师,每周 90 分钟 AI 课程,使用学校笔电和 Gemini Pro。学期末完成研究者自编的 9 情境问卷(A–D 有序选项并写理由),另有课堂观察。监护人书面同意。
- 证据强度
- 低—中。优点是直接研究青年、真实课堂且结合量化与书面说明。局限是一校、n=47、量表未经独立验证;定性材料由单一作者编码,观察未与个体问卷联结;无对照与纵向追踪。结果为描述与关联,不能说明课程造成改变,也不宜外推到其他年龄、文化或工具。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 分别记录“能否检查输出”和“能否追问训练数据、隐私、默认设置与激励”。小规模尝试:同一题先做来源核查,再画出“输入了什么—谁可能保存—谁受影响”的数据路径,比较两种主体性是否一致。
- 青年独立探索
- 候选探索 ①“不熟悉主题时,我能否拒绝一个听起来合理的 AI 答案?”起步:各问一个熟悉与陌生问题,记录核查路径。②“改动多少、改动哪一部分,才会让我觉得作品是我的?”起步:保留一次接受、拒绝、重写的选择,分别记录作者感与控制感;不从感受推断法律所有权。
2. Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy 已核实(全文)
- 作者/机构
- Wei Xiao、Cheng-Hao Men、Jia-Cong Zhang;Shanghai Normal University、Shandong University。
- 类型与状态
- 已同行评审 Original Research,Frontiers in Psychology;正式发表 2026-10-28;全文已核验。
- 原始来源
- frontiersin.org — 全文
- DOI
- 10.3389/fpsyg.2026.1975833
- 查阅日期
- 2026-10-30
- 核心发现
- 四项在线实验显示,较高 AI 协助或高委托工作流会增加认知卸载和“元认知脱离”,并在部分任务中降低专家评定的知识创新质量。实验 1 高协助组的元认知脱离更高,F(1,144)=16.85, p<.001, η²=.105。实验 2 高协助提高脱离(d=.71)和卸载(d=1.08),但卸载是测量而非操纵,中介方向不能完全确认。实验 3 的高委托流程组专家评分较低(M=3.95 vs 4.78, d=.75)且脱离更高(d=.95)。实验 4 表明负向间接效应在复杂任务更强、在较高 AI literacy 时较弱;较高 literacy 条件的间接效应置信区间包含零。
- 方法与样本
- Credamo 招募的中国成年参与者,四个独立在线实验总 N=728:n=150 智能家居创新;n=162 社区服务创新;n=176 城市公共空间更新;n=240 的 2×2 卸载×任务复杂度实验。前三项平均年龄约 29 岁。实验 3 由 3 位盲评专家评价新颖性、实用性与可发展性,ICC=.84。
- 证据强度
- 中(限于短期成人工作流)。随机操纵支持部分短期因果判断,且包含专家结果评分;但实验 3 把回答细节、努力和责任等捆绑在同一工作流,无法确定哪个成分起作用。完整的连续机制没有在同一样本一次检验;新自陈量表、在线短任务、无未成年人和无长期学习结果,限制向青年 Study 外推。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 保留规划、监控、反思的可见痕迹。小规模尝试:同一小研究或创作任务做两次,一次允许 AI 给完整方案,一次只允许事实提示;比较成品,也比较参与者能否说出一个假设、一个被拒选项和一个下一步。
- 青年独立探索
- 候选探索 ①“哪些 AI 帮助释放注意力,哪些直接拿走思考?”起步:把每次使用标为记忆、整理或判断,并写一句理由。②“任务越复杂,流畅答案是否越容易让我停止检查?”起步:各做一个熟悉与陌生小题,关闭 AI 后独立复述推理。
3. Development and validation of the AI cognitive offloading scale for Chinese college students 已核实(全文)
- 作者/机构
- Hongying Chang;School of Educational Sciences, Minzu Normal University of Xingyi,中国。
- 类型与状态
- 已同行评审 Original Research,Frontiers in Psychology;正式发表 2026-10-27;全文已核验。
- 原始来源
- frontiersin.org — 全文
- DOI
- 10.3389/fpsyg.2026.1884676
- 查阅日期
- 2026-10-30
- 核心发现
- 研究形成 20 题量表,把 AI 认知卸载分为记忆卸载、辅助卸载与思考卸载。三维结构在独立样本获得确认,并报告内部一致性、聚合/效标效度与性别测量不变性。各维度与 AI dependence 正相关、与 metacognitive monitoring 负相关;总分与辅助卸载与 need for cognition 负相关,但记忆与思考卸载分别和 need for cognition 的关系不显著。量表测量自报倾向,不直接测量自主性、学习或创作结果;相关不能解释因果。
- 方法与样本
- 研究 1 为中国大学生便利网络样本 n=665,用于项目分析与探索性因素分析;研究 2 为独立便利样本 n=1,123,用于验证性因素分析、信效度和不变性检验。效标包括 AI dependence、need for cognition 与 metacognitive monitoring。
- 证据强度
- 中(作为初步量表开发);低(若用于判断 AI 卸载造成何种结果)。两个大型独立样本与 EFA/CFA 是优点;但均为中国大学生便利样本、自陈且横断面,维度高度相关,严格区分效度仍需复核。没有未成年人,文化与年龄外推有限。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 不要把所有 AI 使用压成一个“依赖”分数;先问交出了哪种功能:记忆/保存、组织/执行、判断/决定。小规模尝试:用可选的三问日志记录三类委托,不作诊断、不排名。
- 青年独立探索
- 候选探索 ①“我最常把哪种认知功能交给 AI?”起步:给五次使用标 M(记忆)、A(辅助)、J(判断)。②“交出判断后,我还能解释并反对建议吗?”起步:面对一个 AI 建议,先写自己的决定,再看 AI,记录是否改变及理由。
4. Catalysts, Not Only Constraints: Pre-Service EFL Teacher Agency in Generative AI-Assisted Lesson Planning 已核实(全文)
- 作者/机构
- Cihat Atar(Yildiz Technical University)、Serdar Tekin(Nevsehir Haci Bektas Veli University),Türkiye。
- 类型与状态
- 已同行评审 Original qualitative case study,Behavioral Sciences 16(11):1998;正式发表 2026-10-26;全文已核验。
- 原始来源
- mdpi.com/2076-328X/16/11/1998
- DOI
- 10.3390/bs16111998
- 查阅日期
- 2026-10-30
- 核心发现
- 当生成式 AI 给出通用、陈套或现场不可行的建议时,准教师通过三类行动体现专业主体性:把内容重新置入本地情境、替换/拒绝不合适建议、课堂中即兴调整。58 人都改过至少部分 AI 方案,但无人完全停止使用工具。论文报告参与者×工具层面的常见问题次数:情境缺口 90、陈套活动 68、不合适/无聊 61、时间安排 45;这些是描述性计数,不构成工具间效果比较。
- 方法与样本
- 一所土耳其州立大学 58 名英语教育专业毕业班准教师,21–27 岁。每人用 ChatGPT GPT-4o、Gemini 2 Flash、DeepSeek-V3 各做一份课案,并在实习学校教学;分析 58 篇反思文章,并观察 13 节课。33 人在高中、25 人在初中实习,但未收集学校学生的数据。
- 证据强度
- 低—中(描述性)。真实规划到实施、反思与部分观察互证是优点;局限为单一便利样本、只观察 13 节课、反思为计分作业且提示直接要求描述改动,可能有迎合与启动效应。工具、主题和顺序由参与者自选且顺序未记录,无法比较工具;无学生学习结果。参与者是成年人,不能直接外推青年。
- 对 Earth Library / Study 的启发
- YuCROWN 推论 / 候选探索 把 AI 的“缺少情境”变成判断练习:创作者指出哪里“不像我的人、地方或经验”,再选择保留、改写、拒绝或现场调整。小规模尝试:让青年给一段关于本地观察的通用 AI 文本标一处保留、一处改写、一处拒绝并说明理由;只分享其自愿发布的最终作品。
- 青年独立探索
- 候选探索 ①“哪些细节只有身处这里的人能补上?”起步:划出三句通用话,换掉一句为亲自观察。②“拒绝 AI 建议是否比会写复杂提示更能显示作者身份?”起步:保存一条被拒建议和理由,同时记录主观作者感;不据此判断法律权利。
这四项研究共同提示:输出层判断、系统层质疑、认知功能委托、元认知参与和情境化改写,是相邻但不同的 agency 维度。只有第 1 项直接支持七年级情境;其余应作为青年探索的假设,而非已验证结论。对 Study,适合保留少量决策痕迹,不把“用了多少 AI”当成单一代理指标;对 Earth Library,展示自愿分享的成品时,可由创作者选择附上“我保留/改写/拒绝了什么”,但不要求披露私人过程。
Owning what one did not make: psychological ownership, agency, and the authorship legitimation deficit in human–AI co-creation(Dong 等;DOI 10.3389/fpsyg.2026.1928186)已在第二期按 2026-10-19 接受、仅摘要可读的 Conceptual Analysis 收录。正式全文于 2026-10-28 发表,本次 2026-10-30 已阅读全文。它仍是概念分析、没有样本或新实证结果,因此不重复推荐。正式全文:frontiersin.org — 全文。保留第二期原收录和查阅日期,并明确新增的是发表状态与全文可得性。
本期去重清单
- 10.1186/s40561-026-00464-y — Student epistemic agency in AI-mediated practices
- 10.3389/fpsyg.2026.1975833 — Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy
- 10.3389/fpsyg.2026.1884676 — Development and validation of the AI cognitive offloading scale for Chinese college students
- 10.3390/bs16111998 — Catalysts, Not Only Constraints: Pre-Service EFL Teacher Agency in Generative AI-Assisted Lesson Planning
2026-10-23 · 第二期
第二期主题:技术语言、迭代控制、阶段性表现,以及署名与责任角色,是不同的变量,需要分开观察。以下“心理所有权”“作者感”均指具体研究或概念分析中的主观经验,不构成法律所有权判断。
检索窗口:重点核验 2026-10-19 至 2026-10-23 新发表或实质更新的内容;并已用 DOI、规范化标题及版本关系,与本页 A–L、21 项基础来源和 2026-10-18 首期 4 项逐一去重,以下 4 项无重复。本期 2 项有全文(第 1、2 项),2 项当前仅有摘要(第 3、4 项)——后两项未阅读全文,其方法、测量与论证细节均未核验。本期不用旧文凑数。本期没有未成年人样本。每项“启发/探索”均为 YuCROWN 推论/候选探索,不是原研究发现。
1. Prompt specificity and iterative behaviour in text-to-music co-creation: a secondary analysis of interaction logs and a framework for perceived creative control 已核实(全文)
- 作者
- Xian Zhang、Lu Pan(Xihua University;Shenyang Conservatory of Music)
- 类型与状态
- 已同行评审的 Original Research,Frontiers in Psychology;正式发表 2026-10-21;全文已核验。
- DOI
- 10.3389/fpsyg.2026.1965465
- 查阅日期
- 2026-10-23
- 方法与样本
- 对 PAGURI 已发布日志的二次分析:304 条文本生成音乐提示词,来自 24 名参与者(原研究平均年龄 26.9 岁,多数为意大利人、音乐参与度较高、此前文本生成音乐经验有限);按自报角色分为 producer/technical n=6、performer n=10、non-performer/other n=8。用词典标记计算技术/乐器具体性,用连续相邻提示词的 Jaccard 相似度度量迭代,并以 TF-IDF cosine 做稳健性核查;推断使用参与者级汇总,避免把 304 条提示当作独立样本。
- 观察到的结果
- 按自报角色分组的参与者,在提示词技术/乐器具体性上差异明显(参与者层面平均具体性 H(2)=10.25, p=.006, ε²=.39;具体性密度 H(2)=11.52, p=.003, ε²=.45),但组间 refinement rate 并不不同(H(2)=.24, p=.888);提示词具体性也未随一次会话系统上升(平均个体内 Spearman ρ=-.06;W=89.0, p=.374)。这说明“能说出更专业的术语”与“能持续把自己的意图推进下去”是两件事。
- 证据强度与局限
- 证据:低—中。优点:公开语料、可复算、参与者级统计和阈值敏感性分析。局限:n=24;角色不是经验证的专长量表;单一系统、约一小时会话;词典与表层相似度不是创造意图或控制感的有效直接测量;没有学习结果。不能据此作因果结论,不能外推到未成年人。
- 启发
- YuCROWN 推论 / 候选探索 在记录 AI 协作过程时,把“我能否准确说出想要什么”与“我能否保留并推进一个方向”分开记录。小规模尝试:一次 15 分钟的声音/图像/文字实验中,青年先写一句意图,每轮标记“继续深化”或“换方向”,最后自己指出哪一轮仍最像自己的选择;不要求公开,是否分享由本人决定。
- 青年探索
- 候选探索 a. 术语更多时,作品真的更接近原意,还是只是更像工具能识别的语言?起步:同一意图写一个日常版和一个术语版提示,盲听/盲看后写下差异。b. 什么时候“重来”是放弃控制,什么时候是主动判断?起步:保留 3 轮记录,每次只写一句换方向的理由。
- 来源
- frontiersin.org — 全文
2. A diagnostic human agency framework for AI driven education from a PRISMA guided systematic review 已核实(全文)
- 作者
- Kandarp Acharya、Mili Gondaliya
- 类型与状态
- 已同行评审的 Review/概念框架,Discover Education 5, article 1206;正式发表 2026-10-21;开放全文已核验。
- DOI
- 10.1007/s44217-026-02173-7
- 查阅日期
- 2026-10-23
- 方法与样本
- PRISMA 引导检索,2018-01 至 2026-03;OpenAlex + UNESCO/OECD/EU 资料库 + 引文追踪。2,910 条记录,去重后 2,255,最终 35 项进入定性综合;62 个代码,经开放/轴心编码形成四维框架。5 名实践者(2 名 K–12 教师、1 名企业学习发展经理、1 名教育技术开发者、1 名国家技能机构政策顾问)评审和修订;两个案例只作说明性应用。编码一致性 Cohen’s κ=.83。
- 核心发现
- 作者把 AI 教育中的 agency 综合为四个相互关联的诊断维度:Purpose(学习者能获得和追求什么有价值的能力)、Control(谁控制路径、数据与退出权)、Collaboration(AI 是否增进或替代人与人协作)、Conditions(隐私、公平、长期支持等结构条件)。作者明确说明,该框架(HAF)目前只有概念效度,尚未经过真实场景的经验验证。
- 证据强度与局限
- 证据:中(作为概念地图),低(作为效果证据)。优点:检索流程透明、跨来源综合、有编码一致性和实践者复核。局限:仅 35 项、没有正式质量评价、仅英语文献、以西方/全球北方材料为主、实践者只有 5 人、案例不是效果评估,HAF 尚未验证。
- 说明
- 这里的 control 包括路径、数据、退出与删除;它不等于法律著作权。论文也没有测量主观作者感。
- 启发
- YuCROWN 推论 / 候选探索 可把四维框架缩成发布前自问,而不是打分工具:这件作品表达了谁在意的目的?谁可更改/撤回/删除数据?AI 是否挤掉了真实回应?参与条件是否公平可持续?小规模尝试:选一个自愿分享的条目,由创作者自己用四个问题各写一句;若不愿公开,记录只留给本人。
- 青年探索
- 候选探索 a. 哪种“选择”会真的改变过程,哪种只是界面上的假选择?起步:列出一次 AI 使用里的所有按钮/决定,圈出改变目标、内容或公开范围的三项。b. 当 AI 帮助更多时,人与人的回应是否减少?起步:同一周比较一次“AI 先回应”和一次“同伴先回应”,只记自己随后改了什么。
- 来源
- link.springer.com — 开放全文
3. Owning What One Did Not Make: Psychological Ownership, Agency, and the Authorship Legitimation Deficit in Human–AI Co-Creation 仅摘要 · 全文待核查
- 作者
- Yanjiao Dong、Ziran Zhou、Yueh-Chih Hsiao、Qi Cao(Anyang Normal University;National University of Singapore;National Taiwan Normal University;Hanyang University)
- 类型与状态
- 同行评审后已接收的 Conceptual Analysis,Frontiers in Psychology;接收日期 2026-10-19。Frontiers 页面明确“final, formatted version will be published soon”;当前仅核验摘要,不标为已有全文的正式版本。
- DOI
- 10.3389/fpsyg.2026.1928186
- 查阅日期
- 2026-10-23(仅摘要)
- 方法与对象
- 概念分析;摘要提到历史上的委托/分布式制作先例和角色相对的分类;无参与者、无实验。由于全文尚未公开,无法核验资料选择、分析步骤或完整论证边界。
- 核心主张
- 作者把 authorship 拆成至少三种关系:体验为“这是我的”、感到自己是行为主体、对发布与后果承担责任;并提出“authorship legitimation deficit”,指某个作者身份称谓超过既有领域对体验或贡献依据的期待。方向设定、选择、编辑和发布决定也可能支持某种作者角色,不能只用初始制作劳动来判断。
- 证据强度与局限
- 实证强度不适用;概念启发为中,结论确定性低。同行评审不会把概念命题变成实证效果;其命题仍待检验。
- 说明
- psychological ownership / felt ownership 是主观经验,不等于版权或其他法律所有权。
- 启发
- YuCROWN 推论 / 候选探索 作品页面可分开记录“我做了哪些决定”“我愿意为哪部分署名/负责”“我是否愿意公开”,不要用一个 author 标签吞并所有关系。小规模尝试:创作者可自选一个更准确的角色标签,如“原始经验提供者 / 导向者 / 编辑者 / 发布决定者”,也可拒绝标签。
- 青年探索
- 候选探索 a. 如果作品主要由 AI 执行,但主题、取舍和公开决定来自你,你愿意怎样署名?起步:对同一作品写 3 个不同署名,并说明最不舒服的一个。b. “这是我的”与“我愿意为它负责”会在什么情况下分开?起步:用两条 1–5 分量表分别记录,不公开也可以。
- 来源
- frontiersin.org — 摘要
- 版本更新
- 正式全文已于 2026-10-28 发表,2026-10-30 已核验全文(见第三期“版本更新”);仍为概念分析。本条原收录与查阅日期(2026-10-23,仅摘要)保持不变。frontiersin.org — 全文
4. Generative AI Configurations Across Creative Phases: A 2 × 2 Experiment on Creative Performance and Immediate Knowledge in Art and Design Education 仅摘要 · 全文待核查
- 作者
- Yiming Wei、Sijun Li(Xi’an Academy of Fine Arts)
- 类型与状态
- 同行评审后已接收的 Original Research,Frontiers in Psychology;接收日期 2026-10-20。Frontiers 页面明确最终排版版本将发布;当前仅核验摘要。
- DOI
- 10.3389/fpsyg.2026.1971021
- 查阅日期
- 2026-10-23(仅摘要)
- 方法与样本
- 2×2 组间实验,N=120 艺术与设计学生;任务为标准化文化遗产色彩重构,测量创造表现、即时任务知识和人机互动过程。摘要未给出招募、年龄、随机化实施、测量工具、盲法、效应量和完整统计计划——这些均未核验。
- 观察到的结果
- 学生被分配到 Text→Text、Text→Image、Image→Text、Image→Image 四个顺序。文本对话型 AI 在发散阶段的创造表现高于图像生成型,并经 Holm 校正仍显著;图像生成在收敛阶段的美学质量差异,未经家族错误率校正后不再显著。预设的 Text→Image 序列没有提高整体创造表现(contrast=.066, p=.671;交互 p=.541),尽管即时知识得分更高。分阶段优势没有自动累积成更好的最终作品。
- 证据强度与局限
- 证据:暂定低—中。实验结构和多重比较校正是优点,但当前仅有摘要,关键实施与测量细节未知;单一任务、即时结果、艺术设计学生,不代表长期学习,也不能外推到未成年人。
- 说明
- 研究没有测量法律所有权;摘要也未报告主观作者感。
- 启发
- YuCROWN 推论 / 候选探索 把 AI 安排在不同学习阶段分别观察,不假定“先文字后图像”就是通用最佳流程。小规模尝试:同一小任务用 A“先自己写两个方向 → AI 追问 → 自己制作”和 B“AI 先出图 → 自己选择修改”两条路径,分别记录新想法数、最终理由和离开 AI 后能否独立解释。
- 青年探索
- 候选探索 a. AI 在哪个阶段帮助你扩展可能,在哪个阶段反而让你过早收敛?起步:把过程分为探索/选择两段,各写一个“AI 帮了什么、挡了什么”。b. 当最终作品相近时,哪条路径让你更能解释自己的决定?起步:完成后关掉 AI,用 60 秒口述三个关键取舍。
- 来源
- frontiersin.org — 摘要
- 版本更新(第四期)
- 2026-11-04 已正式发表,2026-11-06 已核验全文;正式全文更新见第四期。本条原收录与查阅日期(2026-10-23,仅摘要)保持不变。
原研究共同提示:技术语言、迭代控制、阶段性表现、主观作者/责任角色不是同一个变量;某阶段或某指标更好,不等于整体学习、长期能力或作者感更强。
YuCROWN 推论:Study(Human-Centered AI Learning & Research)可以继续用“提出问题—观察记录—整理发现—表达—带给他人—听见回应”,同时给每一步加两个轻量记录:“这一步谁作了不可替代的决定?”“离开 AI 后我能否解释并继续?”这只是待测试的设计假设,不是已证实成效。
分栏原则:法律所有权、主观心理所有权、署名角色、发布控制权必须分栏,不互相代替。
适用范围:本期没有未成年人样本;向青年尤其未成年情境的外推都受限。第 3、4 项仅核验摘要,其结论待全文核对。
本期 DOI / 标题清单
- Prompt specificity and iterative behaviour in text-to-music co-creation: a secondary analysis of interaction logs and a framework for perceived creative control — 10.3389/fpsyg.2026.1965465(全文)
- A diagnostic human agency framework for AI driven education from a PRISMA guided systematic review — 10.1007/s44217-026-02173-7(全文)
- Owning What One Did Not Make: Psychological Ownership, Agency, and the Authorship Legitimation Deficit in Human–AI Co-Creation — 10.3389/fpsyg.2026.1928186(仅摘要)
- Generative AI Configurations Across Creative Phases: A 2 × 2 Experiment on Creative Performance and Immediate Knowledge in Art and Design Education — 10.3389/fpsyg.2026.1971021(仅摘要)
2026-10-18 · 首期
首期主题:作品更精致、感觉更有能力、保有自己的表达,需要分开观察。以下创造性与所有权感均为具体研究中的测量,不构成法律所有权判断。
检索主要窗口为 2026-09-18 至 2026-10-18;本期属于有范围精选,不是系统综述。以下 4 项均已阅读原始全文(4/4)、核实发表状态,并与本站 A–L 及 M 核对后确认不重复(此前无更早精选期次)。本期未收录单纯观点文章或未经评审预印本,不为类型配额凑数。大学/成人样本不能直接代表未成年人。每项“启发/探索”均为 YuCROWN 推论/候选探索,不是原研究发现。
1. Drawing the Line: Where AI Guidance and Human Creativity Meet in Emotion-Driven Comic Storyboarding 已核实
- 作者
- Jocelyn Shen、Isabella Pu 等(MIT/Media Lab)
- 类型与状态
- 同行评审会议研究,ACII 2026;会议 9 月 8–10 日;开放稿 2026-10-06(arXiv v1)。会议日期与仓储日期不同。阅读的是开放全文,非仅摘要。
- ID / DOI
- arXiv:2610.09155v1 · 10.48550/arXiv.2610.09155(仓储 DOI,非 IEEE 出版 DOI)
- 查阅日期
- 2026-10-18
- 方法与样本
- 25 名 18–29 岁非职业创作者;平衡顺序的被试内比较,分别用 EmoToon 与徒手绘制不同的个人故事。
- 观察到的结果
- 问卷及访谈显示:AI 辅助下,自评情感表现与美观更高,主观创作所有权感更低。
- 证据强度与局限
- 证据:中(限短时主观体验)。样本小、故事不同、主要为自评;不能证明长期学习,也不能证明未成年人的变化。
- 启发
- YuCROWN 推论 / 候选探索 Earth Library 可让作者自愿附上“我原本想表达的感受/我保留或改掉了什么”,将画面质量与表达自主性分开观察。
- 青年探索
- 候选探索 AI 让我的故事更像自己,还是更像常见图像?起步:选不敏感的生活片段,先写三句自己的描述,再比较一个 AI 建议,记录一处保留与一处拒绝;可私密保存,不要求公开经历。
- 来源
- 原文(arXiv HTML) · 元数据 · 会议收录核验 · 双盲评审规则
2. The impact of human-AI co-creation on perceived creativity in generative AI art: a serial mediation mechanism of psychological ownership, perceived agency, and creative flow 已核实
- 作者
- Chenyang Wang、Miao Chen、Wen Yang
- 类型与状态
- Frontiers in Psychology,2026-10-09;同行评审原始研究。
- DOI
- 10.3389/fpsyg.2026.1986050
- 查阅日期
- 2026-10-18
- 方法与样本
- 两项随机分组的在线实验,共 600 名中国参与者,平均年龄约 24 岁。
- 观察到的结果
- 共创比 AI 单独生成的主观创造性评分高;第二项实验所测三种工作流中,中间条件评分最好。
- 证据强度与局限
- 证据:中。多种操作权限被捆绑,部分任务产物不等同,不能推出“AI 参与一半最佳”;同步测量的心理中介变量不能证明因果链;无未成年人结论。测量的是心理所有权,不是版权。
- 启发
- YuCROWN 推论 / 候选探索 Study 可先比较“作者能做哪些实质选择”,而非按 AI 使用比例划分好坏;可自愿记录最终选择理由。
- 青年探索
- 候选探索 改变建议内容的权限,还是仅挑选最终图片,更影响“这是我的作品”的感觉?起步:在一个小作品中分别尝试“只选”和“可修改”,记下自己的判断;这是个人探索,不是随机实验的复现。
- 来源
- frontiersin.org — 全文
3. How generative AI becomes embedded in design learning: a 14-instructional-day intensive longitudinal study of reciprocal dynamics between AI use and self-efficacy 已核实
- 作者
- Yang Li、Yi Yang、Silu Bao
- 类型与状态
- Frontiers in Psychology,2026-10-14;同行评审原始研究。
- DOI
- 10.3389/fpsyg.2026.1975841
- 查阅日期
- 2026-10-18
- 方法与样本
- 260 名 18–23 岁中国设计本科生;14 个教学日的日记,共 3,558 次记录;贝叶斯动态结构方程分析。
- 观察到的结果
- AI 使用与下一教学日的自我效能之间,存在小幅的双向预测关联。
- 证据强度与局限
- 证据:中(关联),低(因果解释)。自报、短期、非随机;信心不等于实际能力或独立迁移;没有未成年样本。
- 启发
- YuCROWN 推论 / 候选探索 Study 可分开记录“今天更有信心”和“明天能否自己解释一个选择”,不要把使用频率当成依赖或成长的证据。
- 青年探索
- 候选探索 我什么时候更想用 AI——卡住时,还是有信心时?起步:自愿做五次一分钟私密笔记,记录任务、求助原因和自己的解释;不据个人日记作普遍因果推断。
- 来源
- frontiersin.org — 全文
4. Ways Generative AI Can Support and Threaten Learning in K-20 US Education 已核实
- 作者
- Panayiota Kendeou、Jeffrey A. Greene、Nia Nixon 等
- 类型与状态
- University of Minnesota CEHD 官网,2026 年 10 月(未标具体日)。机构发布的专家共识研究报告;不是已核实同行评审的期刊论文,无已核实 DOI。
- 查阅日期
- 2026-10-18
- 方法与样本
- 结构化 Delphi 方法;初轮 30 名专家,第二轮 29 人参与、28 人提供量化评分;梳理出 65 种学习过程。
- 观察到的结果
- 专家认为风险与支持机会随学习环节而异,不能只看最终产出。
- 证据强度与局限
- 证据:中(共识地图),低(项目效果的因果证明)。专家选择与美国教育情境有局限;这不是学生干预试验,专家同意率不等于效果量。
- 启发
- YuCROWN 推论 / 候选探索 Study 可试“先观察并提出自己的问题 → 用 AI 澄清与比较 → 关掉 AI 再说明理解 → 听取一个人的回应”,作为待检验方案。
- 青年探索
- 候选探索 AI 在哪一步帮助我理解,在哪一步替我跳过了思考?起步:以小船或生活观察为主题,保留一条原始问题、一条核实来源和一段独立解释;不宣称该流程已被本报告验证。
- 来源
- cehd.umn.edu — 专家共识报告
这些研究的方法和对象各不相同,不合并效应量,也不构成 EarthLinC 或 YuCROWN 的成效证据。可供 Cali 讨论的最小探索是:自愿保存“原问题、一次关键取舍、独立解释、他人回应”四个片段,由作者自己选择是否分享。AI 辅助过程的作者感、署名、经验分享许可与法律所有权应分别处理;本期没有对法律权利作出新的结论。对未成年人项目的适用性仍需另行评估。
去重索引
| 日期 | 规范化原文标题 | DOI / ID | 版本 / 状态 | 与既有关系 | 核查日期 |
|---|---|---|---|---|---|
| 2026-10-06(开放稿) | Drawing the Line: Where AI Guidance and Human Creativity Meet in Emotion-Driven Comic Storyboarding | arXiv:2610.09155v1 · 10.48550/arXiv.2610.09155 | ACII 2026 同行评审会议研究;arXiv v1 开放稿 | 新增;EmoToon 开放稿与 ACII 为同一研究,下期不得再作新论文推荐 | 2026-10-18 |
| 2026-10-09 | The impact of human-AI co-creation on perceived creativity in generative AI art: a serial mediation mechanism of psychological ownership, perceived agency, and creative flow | 10.3389/fpsyg.2026.1986050 | Frontiers in Psychology,同行评审原始研究 | 新增;与 A–L 及 21 项来源不重复 | 2026-10-18 |
| 2026-10-14 | How generative AI becomes embedded in design learning: a 14-instructional-day intensive longitudinal study of reciprocal dynamics between AI use and self-efficacy | 10.3389/fpsyg.2026.1975841 | Frontiers in Psychology,同行评审原始研究 | 新增;与 A–L 及 21 项来源不重复 | 2026-10-18 |
| 2026-10(未标具体日) | Ways Generative AI Can Support and Threaten Learning in K-20 US Education | 无已核实 DOI;CEHD 固定 URL | 机构发布的专家共识研究报告,非已核实同行评审 | 新增;与 A–L 及 21 项来源不重复 | 2026-10-18 |
| 2026-10-21 | Prompt specificity and iterative behaviour in text-to-music co-creation: a secondary analysis of interaction logs and a framework for perceived creative control | 10.3389/fpsyg.2026.1965465 | Frontiers in Psychology, peer-reviewed Original Research | 新增,无历史重复 | 2026-10-23 |
| 2026-10-21 | A diagnostic human agency framework for AI driven education from a PRISMA guided systematic review | 10.1007/s44217-026-02173-7 | Discover Education, peer-reviewed Review/conceptual framework | 新增,无历史重复 | 2026-10-23 |
| 2026-10-19(接收) | Owning What One Did Not Make: Psychological Ownership, Agency, and the Authorship Legitimation Deficit in Human–AI Co-Creation | 10.3389/fpsyg.2026.1928186 | Frontiers in Psychology, peer-reviewed accepted Conceptual Analysis; abstract-only | 2026-10-23 按已接受摘要收录;2026-10-28 正式全文发表;2026-10-30 全文核验;同一研究版本更新,不另计新推荐 | 2026-10-23 |
| 2026-10-20(接收) | Generative AI Configurations Across Creative Phases: A 2 × 2 Experiment on Creative Performance and Immediate Knowledge in Art and Design Education | 10.3389/fpsyg.2026.1971021 | Frontiers in Psychology, peer-reviewed accepted Original Research; abstract-only(2026-10-23);现状:2026-11-04 正式发表,全文已核验(2026-11-06) | 新增;正式全文出现时记录为版本更新,不作为新研究重复推荐;2026-11-04 已正式发表(Frontiers in Psychology,同行评审 Original Research),2026-11-06 全文核验(见第四期);原 2026-10-23 仅摘要查阅日期保留 | 2026-10-23 |
| 2026-10-24(在线)/ 2026-10-26(VoR) | Student epistemic agency in AI-mediated practices | 10.1186/s40561-026-00464-y | Smart Learning Environments,同行评审 Research article;全文已核验 | 新增,无历史重复 | 2026-10-30 |
| 2026-10-28 | Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy | 10.3389/fpsyg.2026.1975833 | Frontiers in Psychology,同行评审 Original Research;全文已核验 | 新增,无历史重复 | 2026-10-30 |
| 2026-10-27 | Development and validation of the AI cognitive offloading scale for Chinese college students | 10.3389/fpsyg.2026.1884676 | Frontiers in Psychology,同行评审 Original Research;全文已核验 | 新增,无历史重复 | 2026-10-30 |
| 2026-10-26 | Catalysts, Not Only Constraints: Pre-Service EFL Teacher Agency in Generative AI-Assisted Lesson Planning | 10.3390/bs16111998 | Behavioral Sciences 16(11):1998,同行评审 Original qualitative case study;全文已核验 | 新增,无历史重复 | 2026-10-30 |
| 2026-11-03 | Who Is Doing the Thinking? Characterising Human Agency in First-Year Students’ AI Interactions | arXiv:2611.04783v1 · 10.48550/arXiv.2611.04783 | arXiv v1 预印本;同行评审未核实;全文已核验 | 新增;与 A–L、21 项来源及前三期无重复 | 2026-11-06 |
| 2026-11-04 | Measuring the collaboration, not the agent(全文扩展标题:Measuring the Collaboration, Not the Agent: An Evaluation Framework for Human–AI High-Dimensional Visual Analytics) | arXiv:2611.06487v1 · 10.48550/arXiv.2611.06487 | arXiv v1 预印本/评价框架与模拟研究;IEEE VIS 2026 VISxGenAI 工作坊节目列入(11/09),同行评审未核实;全文已核验 | 新增;两种标题为同一研究,不可日后再当新研究推荐 | 2026-11-06 |
| 2026-11-02 | CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances | arXiv:2611.03389v1 · 10.48550/arXiv.2611.03389 | arXiv v1 开放稿;作者官网标 IEEE IS2 2026 Accepted;评审与正式 DOI 未核实;全文已核验 | 新增;arXiv v1 与作者会议稿为同一研究,不可日后再当新研究推荐 | 2026-11-06 |
| 2026-11-09 | Generative AI use and scientific literacy among 15-year-olds in Beijing, Shanghai, Jiangsu, and Zhejiang, China: the mediating role of academic help-seeking | 10.3389/fpsyg.2026.2013021 | Frontiers in Psychology,同行评审 Original Research;全文已核验 | 新增;与 A–L、21 项来源及前四期无重复 | 2026-11-13 |
| 2026-11-09 | Perceived Agency During Agent–Avatar Control Transitions in Virtual Reality | arXiv:2611.12529v1 · 10.48550/arXiv.2611.12529 | arXiv v1 预印本;同行评审/正式会议发表未核实;全文已核验 | 新增;同一研究的后续会议稿须合并版本,不可再当新研究推荐 | 2026-11-13 |
| 2026-11-12(接收) | AI and the responsibility-bearing status of authorship | 无可核实 DOI;机构记录 | Philosophy & Technology,同行评审后接收的概念/哲学分析;仅摘要,接受稿 Embargoed | 新增;以规范化标题+作者+机构 URL 去重,正式全文出现时记录为版本更新 | 2026-11-13 |
- 仅更新 M 节:新增第五期(2026-11-13)3 项新资料(2 项全文、1 项仅摘要)、去重索引 3 行及本期去重清单;栏目状态改为已收录五期,下次例行精选为 2026-11-20;前四期内容完整保留。
- 基础参考研究日期 2026-10-03 未被重置;A–L 节全文及 21 项基础来源的日期与核实状态均未改变;旧条目的查阅日期未被统一重新标为核实。
- 侧栏“研究精选更新”改为 2026-11-13(第五期)。
- 仅更新 M 节:新增第四期(2026-11-06)3 项新资料、1 项版本更新、去重索引 3 行及本期去重清单;栏目状态改为已收录四期,下次例行精选为 2026-11-13;前三期内容完整保留。
- 10.3389/fpsyg.2026.1971021 的索引记录已扩展,并在第二期条目中追加正式全文更新注记;原 2026-10-23 查阅日期不变,原文不删改。
- 基础参考研究日期 2026-10-03 未被重置;A–L 节全文及 21 项基础来源的日期与核实状态均未改变;旧研究未被统一标为新核实。
- 侧栏“研究精选更新”改为 2026-11-06(第四期)。
- 仅更新 M 节:新增第三期(2026-10-30)4 项新研究、1 项版本更新、去重索引 4 行及本期去重清单;栏目状态改为已收录三期,下次例行精选为 2026-11-06;首期、第二期内容完整保留。
- 10.3389/fpsyg.2026.1928186 的索引记录已扩展,并在第二期条目中新增正式全文版本注记;原收录日期与查阅日期不变。
- 基础参考研究日期 2026-10-03 未被重置;A–L 节全文及 21 项基础来源的日期与核实状态均未改变。
- 侧栏“研究精选更新”改为 2026-10-30(第三期)。
- 仅更新 M 节:新增第二期(2026-10-23)4 项、去重索引 4 行及本期 DOI / 标题清单;栏目状态改为已收录两期,下次例行精选为 2026-10-30;首期内容完整保留。
- 基础参考研究日期 2026-10-03 未被重置;A–L 节全文、21 项基础来源的日期及核实状态均未改变。
- 核验深度:第 1、2 项已核验全文,第 3、4 项仅核验摘要;没有任何旧条目被重新标为核实。
- 侧栏“研究精选更新”改为 2026-10-23(第二期),与基础参考研究日期分开显示。
- 仅更新 M 节:收录首期(2026-10-18)4 项,并添加去重索引;栏目状态改为“首期已收录”,下次例行精选为 2026-10-23。
- 本期已阅读全文(4/4)。基础参考研究日期 2026-10-03 与 A–L 节全文、21 项来源的日期及核实状态均未改变。
- 侧栏新增“研究精选更新:2026-10-18”,与基础参考研究日期分开显示。
本期仅更新研究精选栏目,不更新基础参考研究日期(2026-10-03),也不改变 A–L 节及 21 项基础来源的日期与核实状态。
What "Human-Centered AI" actually means right now — and what YuCROWN should carry back from it.
This is a repeatable internal reference, not a finished argument and not an exhaustive or systematic review. It is a scoped reading of selected primary sources across research, policy, education, nonprofit/funding, and technical standards, with YuCROWN's own interpretation and open research proposals clearly separated from what each source actually says. It exists to be consulted — when narrative, Study design, funding strategy, or EarthLinC protocol direction need updating — not to be published as-is. This version corrects several unsourced generalizations in v1, adds bounded empirical evidence, verified funding conditions, standards corrections, proposed research methods, a YuCROWN application map with source reconciliation, and a full bibliography.
- What does "Human-Centered AI" mean across research, policy, education, nonprofit/funding, and standards — and how does it relate to AI literacy and responsible AI, rather than replace them?
- What does bounded, source-specific evidence actually show about AI's effects on learning, thinking, and creativity?
- What funding conditions are verifiably open, and what remains unconfirmed?
- Where do existing provenance and credentialing standards stop, and where is that a genuine open design question?
- How might YuCROWN's existing Stations (Fellow, Conversations, Field, Earth Library, Study) connect to this landscape — without claiming endorsement?
- What research questions and methods could Cali actually design around?
- What can YuCROWN honestly claim now, and what remains open?
How to use this reference
使用说明
- NarrativeUse Sections A, B and K for language that is actually defensible — human agency, judgment, authorship — and for what should stay internal.
- Cali / StudyUse Sections B, I and J — bounded evidence, candidate research questions, and participation/consent design — before specifying any method.
- FundingUse Section C for verified conditions and open questions per funder; do not cite amounts or deadlines elsewhere without rechecking the live source.
- Projects / FieldUse Section G's application map to see how a real pilot might connect to Fellow, Conversations, Field, or Earth Library without redefining any of them.
- Protocol / EarthLinCUse Sections E and F for what existing standards already cover and what remains a genuine, untested design question for EarthLinC.
Definitions & Conceptual Frame
概念与定义
Correction from v1: Human-Centered AI (HCAI) is not a replacement for AI ethics/responsible AI, nor a layer added on top of AI literacy once skills are covered. Across the sources reviewed here, it reads as a cross-disciplinary orientation toward human needs, capabilities, values, and social context — one that overlaps with, and draws on, AI literacy, responsible AI, learning sciences, participatory design, and accessibility, rather than superseding any of them. Interpretation
Working glossary (operational, not a consensus taxonomy)
Primary framework sources
UNESCO — AI Competency Framework for Students Verified
- Published
- 2024 (official article dated 2024-08-08); page shows a January 2026 update, which is evidence the page was revised, not that a new 2026 edition of the framework was issued.
- Accessed
- 2026-10-03
- Type
- Institutional competency framework (UNESCO)
- Paraphrase
- Twelve competencies across four dimensions — human-centred mindset, ethics of AI, AI techniques & applications, AI system design — progressing through understand/apply/create levels.
- Supports
- Use of "human-centred mindset" and "co-creator" as sourced, current curricular language.
- Cannot establish
- Effectiveness of the framework in practice, or that a 2026 revision exists.
- Source
- unesco.org — AI Competency Framework for Students [1]
OECD — Digital Education Outlook 2026 Verified
- Published
- 2026-01-19 · DOI 10.1787/062a7394-en
- Accessed
- 2026-10-03
- Type
- Intergovernmental policy synthesis report
- Paraphrase
- Reviews effective uses of generative AI in education; distinguishes short-term task performance gains from durable learning, and stresses pedagogical design as the deciding factor rather than the tool itself.
- Supports
- A policy-level, source-dated basis for "assisted performance is not the same as learning."
- Cannot establish
- Causal effects for any specific classroom, population, or YuCROWN pilot.
- Source
- oecd.org — Digital Education Outlook 2026 [2]
Stanford HAI — About Verified
- Published
- Original publication date not stated on the live page
- Accessed
- 2026-10-03 (live page verified this session — correcting v1, which could not confirm a URL)
- Type
- Institutional "about" page
- Paraphrase
- Describes an interdisciplinary orientation toward human impact, augmentation (not replacement), and human flourishing as research aims.
- Supports
- Stanford HAI as a reference institution for the field's framing.
- Cannot establish
- Any relationship, review, or endorsement between Stanford HAI and YuCROWN.
- Source
- hai.stanford.edu — About [3]
Microsoft Research — People-Centric AI Verified
- Published
- Original date not stated; theme page is actively maintained
- Accessed
- 2026-10-03
- Type
- Corporate research-lab theme page
- Paraphrase
- States research aims of human agency/skill, creativity/collaboration, and equitable representation/participation, with named project lines including Tools for Thought, Project Provenance, and Project Volano.
- Supports
- That "process visibility" and "provenance" are active corporate research interests, not a gap unique to YuCROWN's observation.
- Cannot establish
- That any named project already does what EarthLinC proposes, or that YuCROWN's approach is novel relative to them.
- Source
- microsoft.com/research — People-Centric AI [4]
HCAI, AI literacy, and responsible AI are overlapping lenses on the same shift, not competing labels — AI literacy already carries agency content (e.g., UNESCO's "co-creator" framing), and responsible AI already asks about human oversight. These sources establish that agency, provenance and human capability are active research interests. They do not establish a unique or empty market. A proposed question for Study is which selected process records help people explain their choices and how their usefulness can be tested.
Empirical Evidence on Learning & Cognition
学习与创造力的研究证据
The entries below are bounded: each is one study or working paper, with a specific population, method, and limitation. None should be read as a universal verdict on "AI and learning." A peer-reviewed study is evidence about its own sample and task, not a general law; a working paper is explicitly non-peer-reviewed.
Lee et al. — Impact of Generative AI on Critical Thinking Verified
- Published
- CHI 2025 (Microsoft Research publication page)
- Accessed
- 2026-10-03
- Design
- Survey, 319 knowledge workers, 936 self-reported examples of AI tool use
- Bounded finding
- Higher self-reported confidence in AI was associated with lower reported critical-thinking effort; higher self-confidence in one's own task ability was associated with greater effort.
- Limitation
- Adult knowledge workers, self-report, correlational — no causal claim about cognitive decline, and no youth or classroom data.
- Proposed implication
- Proposed design Study could ask participants why they accepted or rejected a suggestion, rather than labeling frequent AI use itself as "dependency."
- Source
- microsoft.com/research — publication page [5]
Bastani et al. — Generative AI Without Guardrails Can Harm Learning Verified
- Published
- PNAS, 2025 · DOI 10.1073/pnas.2422633122
- Accessed
- 2026-10-03
- Design
- Classroom-randomized controlled trial, ~1,000 high-school students, one school in Turkey; GPT Base vs. a teacher-designed GPT Tutor vs. control, high-school mathematics
- Bounded finding
- Assisted practice scores improved under both AI conditions; unassisted exam scores were about 17% lower for the unguarded GPT Base group; the designed tutor largely mitigated this loss without demonstrating a net positive unassisted-exam gain.
- Limitation
- Single school, single subject (math), short-term; cannot generalize to open-ended arts/creative tasks or to long-term agency.
- Proposed implication
- Proposed design Distinguish assisted output from later independent explanation or transfer in any Study method.
- Source
- pnas.org [6]
Doshi & Hauser — AI Enhances Individual Creativity, Reduces Collective Novelty Verified
- Published
- Science Advances, 2024-07-12 · DOI 10.1126/sciadv.adn5290
- Accessed
- 2026-10-03
- Design
- Randomized short-story writing task with and without AI idea prompts, externally rated
- Bounded finding
- AI-assisted stories were rated more creative individually, especially for writers with lower creative baselines, but the overall pool of stories became more similar to each other.
- Limitation
- Single bounded writing task, external raters, no measure of personal voice development or longer-term authorship.
- Proposed implication
- Proposed design Track diversity and self-reported ownership of choices alongside external polish ratings.
- Source
- pmc.ncbi.nlm.nih.gov [7]
Oreopoulos & Low — One Click Away: AI Tutoring with Khanmigo Needs checking
- Published
- NBER Working Paper 35620, 2026-08 · DOI 10.3386/w35620 — non-peer-reviewed working paper
- Accessed
- 2026-10-03
- Design
- Two-year cluster-randomized trial, 18 Tennessee middle schools, remedial math; Khan Academy plus a coaching tutor vs. usual instruction (per publication listing/search excerpt)
- Status
- Full PDF and NBER paper page returned a 403 on retrieval this session; detailed effect estimates are not independently audited here and must not be quoted numerically until the full text is reviewed.
- Cannot establish (yet)
- Magnitude of effect, or the isolated contribution of the AI component versus coaching.
- Related, not a substitute
- Khan Academy's own research page is implementation/research reporting by the organization itself, not independent replication.
blog.khanacademy.org/research - Source
- nber.org/papers/w35620 [8]
Read together, these four sources resist a single story. AI assistance can raise assisted performance and individually rated creativity while simultaneously producing lower unassisted exam performance in the unguarded condition of one randomized math study, and less collective variety (in one bounded writing task). The adult knowledge-worker survey finds a correlation between higher AI confidence and lower self-reported critical-thinking effort — a correlational finding, not a causal one. "More cognitive effort" is not automatically the goal — assistive reduction of real barriers can be valuable, especially for accessibility. What matters is specifying, in advance, which kind of intellectual work a given project actually cares about (independent transfer? personal voice? collaborative process?), because final output quality and tool engagement alone cannot prove learning, agency, or authorship. Any future Study method should state this target before collecting data, not infer it afterward.
Funding & Collaboration Landscape
资助条件与合作路径
Problem lenses funders appear to use, plus verified current conditions for four named organizations. Readiness and eligibility are separate questions — a lens matching YuCROWN's language is not the same as confirmed fit.
| Funding lens | What funders seem to be asking |
|---|---|
| Youth agency / critical thinking / wellbeing | Are young people keeping their judgment and wellbeing intact as AI becomes ambient? |
| Research–practice partnerships | Can researchers and practitioners co-design and test together, with shared governance? |
| Equitable / rural / place-based access | Who is excluded from these tools and opportunities? |
| Accessibility / inclusion | Does this work for disabled, multilingual, and under-resourced learners from the start? |
| Creativity / culture / civic expression | Does AI expand or flatten creative, cultural, and civic practice? |
| Technology / in-kind support | Can funders contribute tools, credits, or training instead of cash? |
| Program / evaluation capacity | Can the organization actually deliver, measure, and report on this? |
Young Futures — Unwritten Challenge Verified (conditions) Needs checking (fit)
- Eligibility
- U.S.-based tax-exempt nonprofits, schools, or projects with a fiscal sponsor; serving youth ages 10–19.
- Amount / scale
- $50,000–$100,000 per award; minimum $1 million committed across 10+ awards.
- Window
- Applications 2026-09-23 to 2026-10-16, 5:00pm PT / 8:00pm ET deadline.
- Tracks
- Learning/critical thinking/decisions; trusted adults; narrative. AI use is not required.
- Requirements
- Youth input in design, execution, and evaluation; minor-protection measures; concrete evidence tested with youth; a participating organizational leader.
- Excludes
- Projects chiefly benefiting a for-profit product, surveillance uses, and one-off activations.
- Unresolved conflict
- The site's own eligibility quiz states the funded "Academy" begins January 2027, while the timetable/FAQ states April–August 2027. This is a live conflict in the funder's own materials — do not resolve it here; confirm directly with Young Futures before any claim.
- Fit note
- Needs checking — U.S. nonprofit registration alone does not establish eligibility for a project delivered in Taiwan; this must be confirmed directly, not assumed.
- Source
- youngfutures.org/challenges/unwritten [9]
Spencer Foundation — Research-Practice Partnerships Verified (conditions)
- Amount / term
- Up to $400,000 including up to 15% indirect costs; maximum 3 years.
- Geography
- U.S. and international applicants accepted.
- Expectation
- An existing, sustained researcher–practitioner partnership with a jointly developed agenda and shared governance; graduate students may participate but not serve as PI/Co-PI.
- Current window
- Pre-proposal stage is closed; a 2026-10-28 full-proposal deadline applies only to applicants already invited from pre-proposal — not a new open call.
- Fit note
- Proposed design A possible longer-term route if a genuine, sustained partnership and PI/host institution are established — not a present fit. A doctoral researcher's enrollment status does not by itself establish PI eligibility, and no partner institution is confirmed.
- Source
- spencer.org — Research-Practice Partnerships [10]
Google.org — Knowledge, Skills & Learning Needs checking
- Paraphrase
- A philanthropic theme page and a March 2025 update on a prior AI Opportunity Fund cohort — evidence of past giving themes and past recipients, not a currently open, confirmed application pathway for YuCROWN.
- Status
- Landscape monitoring only; no deadline, amount, or eligibility is asserted here.
- Source
- google.org · blog.google — March 2025 update [11]
Microsoft for Nonprofits Needs checking
- Paraphrase
- A program offering technology grants/discounts and nonprofit skilling support — distinct in kind from a research cash award. Terms, pricing, and account-eligibility criteria require direct verification and change over time.
- Fit note
- Proposed design Organizational capacity support, not evidence this funds EarthLinC's commercial development.
- Source
- Microsoft for Nonprofits · eligibility criteria [12]
Nonprofit / Institutional Credibility
非营利组织如何建立可信度
Credibility in education nonprofits is cumulative and multi-layered: legal nonprofit status, financial transparency, documented program design, real institutional partners, participant scale, collected evidence, annual reporting, and — sometimes, later — independent evaluation or university research partnerships. Interpretation The cards below illustrate the pattern with named, source-checked examples; they are not ranked against each other or against YuCROWN.
FIRST / Brandeis longitudinal research Verified (existence) Needs checking (detail)
- Paraphrase
- Official program and university summary pages describe a ten-year follow-up of FIRST participants compared with peers.
- Status
- The direct Brandeis research page returned a 403 on retrieval this session; detailed estimates and methodology are not independently audited here.
- Supports
- An example of cumulative, long-horizon program evidence — not proof of AI-specific effects, and not transferable to YuCROWN's numbers.
- Source
- heller.brandeis.edu · firstinspires.org/about/impact [13]
Khan Academy — efficacy overview Verified (existence)
- Paraphrase
- A support-site overview listing efficacy studies the organization points to for its platform.
- Caution
- Distinguish platform-wide effectiveness claims, combined interventions (e.g., tutor + platform, as in Section B's NBER entry), and AI-specific estimates — these are not interchangeable.
- Source
- support.khanacademy.org [14]
Code.org / CodeAI — accessibility reporting Verified
- Published
- 2026-04 (current page; note the organization now uses the name "CodeAI")
- Paraphrase
- Describes annual third-party accessibility evaluations, a published VPAT, and openly stated remaining conformance gaps against WCAG 2.1 AA.
- Supports
- An example of disclosed limitations as a credibility practice — not universal conformance, and not a learning-outcomes claim.
- Source
- support.code.org [15]
UNICEF — Policy Guidance on AI and Children, v3 Verified
- Published
- 2025-12
- Paraphrase
- Rights-oriented guidance addressing privacy, fairness, inclusion, safety, disability accessibility, and healthy development for children interacting with AI systems.
- Supports
- A sourced reference point for child-focused design principles.
- Cannot establish
- Certification of any specific product or organization, including EarthLinC.
- Source
- unicef.org/innocenti [16]
Legal nonprofit status, a transparency seal, participant scale, and independent evaluation are different kinds of trust. A Candid/GuideStar-style seal signals organizational disclosure, not education accreditation or program efficacy. Nothing in this Station should present YuCROWN as offering accredited certificates or as independently evaluated.
Protocol / Standards Landscape
过程记录与技术标准
Correction from v1: the claim that no existing standard "can represent reasoning" overstated the case. The more accurate statement is that these standards are extensible frameworks — whether they capture reasoning or process depends on what data is fed into them, which none of the reviewed standards mandate by default.
| Standard | Scope / capacity | Remaining design question |
|---|---|---|
| C2PA Content Credentials Verified | Cryptographically bound assertions about content source and edit history. | Authenticity of an assertion is not truth of its content; completeness depends entirely on which systems contribute data. Reasons behind a choice are a separate, optional layer. |
| W3C PROV-DM Verified | An extensible model of entities, activities, agents, derivation, and attribution (Recommendation, 2013-04-30). | Can model process traces if the underlying schema and data are supplied — it does not itself capture subjective experience or evaluate evidence validity. |
| 1EdTech Open Badges 3.0 Verified | Verifiable achievement credentials: issuer, criteria, evidence, assessment, endorsement metadata. | Evidence fields can link to process artifacts, but a badge itself is not accreditation or proof of intellectual contribution. |
| Caliper Analytics 1.2 / ADL xAPI Verified | Structured activity events/statements (actor–verb–object, with optional context and result). | Profiles and extensions can express richer activity, but raw clicks, timestamps, and logs alone cannot establish judgment — semantic coding and participant accounts are needed alongside them. |
Sources: C2PA v2.4 spec [17] · W3C PROV-DM [18] · Open Badges 3.0 [19] · Caliper 1.2 · xAPI data spec [20]
Comparing platform families — original comparison, not sourced claims
Five common families handle different pieces of the puzzle: learning-management/activity logs (what actions, in what sequence); portfolios/public work (finished pieces, loosely dated); provenance/credentials (source and achievement metadata); research diaries/interviews (rich but labor-intensive and rarely portable); AI chat histories (raw interaction text, rarely linked to the resulting work or to a person's later reflection). None of these families is assumed empty by default — a careful audit of any specific product would need to check for version history, interaction logs, consent records, and export functions before concluding a gap exists.
An open, testable question is whether a single, participant-controlled record could connect these pieces for one person's project:
This is a design problem for EarthLinC to test against simpler alternatives (e.g., a diary plus a document), not a claim that such a record is unique, already built, or proven useful.
"Can EarthLinC make the human process visible again around AI-assisted creation and learning — and is that worth the burden of recording it?"Held as a hypothesis, not a conclusion.
EarthLinC as a Research Hypothesis
把 EarthLinC 作为研究假设
EarthLinC is Kong's independent infrastructure, used by YuCROWN — it is not itself a YuCROWN program. Its relevance to Human-Centered AI research is a hypothesis to test through real Study pilots, not a claim to make publicly yet. Proposed design
What would need to be true for this to be meaningful
A record would need to be ethical, consent-aware, portable, and genuinely useful to the person it is about — meaning they can see, understand, and control what is recorded about their process, and the record can travel with them rather than being locked inside one platform. It should also be tested against the question "is this better than a diary and a shared folder?" — not assumed superior by default.
What should not be captured
Anything that compromises privacy, especially for minors; anything collected without clear, age-appropriate, understandable consent; anything that turns a chosen learning process into continuous surveillance rather than a reviewable, opt-in record.
Technical readiness — honestly stated
| Capability | Status |
|---|---|
| Authored Stations and inter-Station linking | Described in existing program sources as present. |
| Voluntary "mint" / crown action | Described in existing program sources as present. |
| Comprehensive version history | Needs checking |
| Full AI-interaction logs with model metadata | Needs checking |
| Permission tiers / role-based access | Needs checking |
| Export, consent ledger, withdrawal & deletion | Needs checking — do not assume deletion exists until verified |
The existing "refine" authoring tool is known to replace a page without a guaranteed version-history trail. A chain pointer or a mint action records that an action occurred — it does not by itself establish legal ownership, learning, the full scope of human contribution, or informed research consent.
A proposed, not-yet-built research record
Proposed design For a future pilot only: a pseudonymous project ID; an optional original observation or question; selected drafts; a short decision note explaining a key choice; an optional disclosure of AI contribution; a participant reflection; a mentor's response; and a sharing permission the participant chooses themselves. Only the minimum subset needed for a specific research question should ever be collected. A participant must be able to decline research participation and still fully create, use, and contribute. Identifiable or sensitive research data should stay off public Stations and any immutable ledger, and retention/withdrawal limitations should be explained before any collection begins — not assumed solved by this reference.
YuCROWN Application Map & Source Reconciliation
与 YuCROWN 各计划的关系
A reference mapping, not a redesign — and not an edit to any other Station. Each row below is an external research signal paired with a possible, undecided internal implication; the reading list that follows maps evidence to actual YuCROWN Stations.
| External research signal | Possible YuCROWN implication |
|---|---|
| Human agency central across frameworks, overlapping with AI literacy | Study should measure judgment and decision-making, not only output quality. |
| Bounded evidence on cognitive offloading (Section B) | Project design should make reasoning and revision visible, not just the finished work. |
| Provenance standards extensible but process-agnostic by default (Section E) | Earth Library / EarthLinC can test a stronger, consent-based process record as a research question. |
| Research–practice partnership models in funding (Section C) | School/community projects could be co-designed as real research contexts over time, not delivery sites — contingent on a genuine sustained partnership. |
| Accessibility named explicitly in standards and guidance (Sections D, E) | Cali's framework should treat accessibility as core from the start, per Section J. |
This reference draws on the user's current explicit instructions, which describe Earth Library as a public environment of independently authored works in relation to human context, and Conversations as a space for young questioners and intergenerational transmission. These current instructions guide this Station's language. They are not silently reconciled here with:
- The OS Station, which still describes Earth Library as a "living collection/curated public program" and Study as a newer research function — OS: Status & Infrastructure.
- The public organization Station, which defines Study more narrowly through professional identity — Connecting Realms of World Narrative.
- Cali's own research-notes brief (2026-10-01), which names a "Human-Centered AI Education and Research Program" within Study — For Cali — Research Notes. This program name should not be unilaterally changed to "Learning and Research" or otherwise redefined by this Station.
No change is made to the OS or any public Station by this reference. A future human review should decide how these definitions are reconciled.
Mapping evidence to existing Stations
Fellow (A Colour Only You Can Leave): original making, voice, and judgment. A research lens here could observe how ideas change and whether choices are explainable — not merely count Stations produced.
Conversations (C — Conversations): per current instructions, young people ask the questions, and experience travels across generations before some of it disappears (ASK → LISTEN → EXPERIENCE → CARRY → REMAIN). A research lens could examine how questions evolve, what younger participants carry into later practice, and what experienced participants feel is missing or misrepresented — not generic dialogue, and not AI-generated "experience." Cali's existing notes still contain generic Conversations wording from an earlier draft; that wording should not be copied into this Station's definition.
Field: concrete practice settings. A research lens should examine real observation and data where it already exists, without inventing corporate partners or locations not already confirmed.
Earth Library (Earth Library): a public space of independently authored works in relation to context. Visibility here is a chosen act, not an automatic research dataset.
Study: per Cali's research-notes brief, a Human-Centered AI Education and Research Program centered on participants' interests, lived experience, and questions, working with teachers, Fellows, and mentors. Formal research questions and methods remain Cali's to design — Section I below offers candidates, not decisions. A KAIST-affiliated biography is a personal credential, not an institutional partnership, and the school proposal (把日子織成星空) remains a proposal, not a confirmed enrollment, partnership, or funding commitment.
Analogy source for Conversations (contextual only — not an endorsement or redefinition): UNESCO, Living Heritage & Education, official page excerpt describes intergenerational transmission as interactive, informal learning; full page retrieval was partially blocked during this session, so this is a source-excerpt-level verification only. ich.unesco.org [21]
What This Research Should Feed
如何用于下一步工作
A practical routing matrix. The central position this Station supports is research and practice around people's judgment, expression, authorship, and relationships in an AI-shaped world — AI is a tool, collaborative medium and research context; people's judgment, expression, authorship and relationships are central.
| Area | Supports now | Next evidence needed | Owner / review |
|---|---|---|---|
| Narrative | Defensible language: human agency, judgment, authorship, process visibility (Sections A–B). | None required to use language already sourced; avoid institutional-alignment claims. | Responsibility to be assigned — not an appointed role. |
| Study | Candidate research questions and method menu (Section I). | Pilot feasibility data before any metric is finalized. | Cali (confirmed lead) |
| Funding | Verified conditions and open questions per funder (Section C). | Direct confirmation with each funder before citing dates/amounts externally. | Responsibility to be assigned — not an appointed role. |
| Projects / Field | Application map (Section G) and the human-process chain (Section E) to watch for. | A minimal, consent-safe version of any process record, tested with real participants. | Responsibility to be assigned — not an appointed role; Cali involved in research design. |
| EarthLinC / protocol | A testable hypothesis list and honesty about current technical readiness (Section F). | An actual technical capability audit, not assumed functions. | Kong Group (secondary to pilot need — responsibility to be confirmed) |
| Website | Only Verified-tagged claims, with sources, may appear publicly. | Re-verification at time of publication, not at time of writing. | Human reviewer before publish |
| Future research | A dated starting map with named gaps (Section L). | OECD, Stanford HAI, NBER full text, FIRST full report, and live funder pages rechecked at source. | Whoever revises this Station next |
Proposed Research Questions & Methods
候选研究问题与方法
Three candidate questions for Cali's review — not promised studies, and not a claim that any are underway. Proposed design
RQ1 — Independent transfer
Under what mentoring and AI-scaffolding conditions can participants explain and transfer a key decision independently later on? Proposed approach: compare an initial task and a later similar task; collect brief verbal or written explanations and participants' own choice of drafts; keep assisted-quality scores separate from independent-understanding scores.
RQ2 — Voice and diversity
How does AI support shape distinctive voice and diversity across a participant's successive projects? Proposed approach: elicit drafts and short decision notes; let participants define "voice" in their own words; use contextual review with multiple raters and a jointly designed rubric; do not use an AI-authorship detector as evidence, since these tools are not validated for this purpose.
RQ3 — What is carried forward
What do young questioners in Conversations carry forward into later choices or practice? Proposed approach: track how a question evolves across a conversation; record the encounter in an account the interviewee approves; follow up later, at a time the participant chooses, for reflection — without treating a single summarized account as settled "truth" about an elder's full experience.
Method menu, roughly in order of ethical and practical ease
Start with a feasibility and participatory study; move to mixed-methods or qualitative case studies with repeated within-person observation; add comparison groups only when genuinely feasible and ethical; consider randomized designs only later, if the question and resources clearly warrant it. Any design should pre-specify its questions, address sampling and recruitment, multilingual translation choices, coding reliability, missing data and dropout, confounders (teacher, time, prior skill), the researcher's own position and conflicts of interest, negative or disconfirming cases, and member-checking with participants. A small pilot can demonstrate feasibility and rich process — it should not be reported as generalized causal proof. No sample size, power calculation, or schedule is specified here; these must be designed with Cali, not assumed.
Operational construct matrix — unvalidated candidates, for discussion only
| Construct | Candidate observable | Explicitly not this |
|---|---|---|
| Agency | Articulated choices and stated reasons | Sheer number of AI suggestions rejected |
| Learning | Independent explanation or transfer on a later task | Polish or quality of a single assisted output |
| Authorship | Self-account, draft history, stated credit | A legal ruling on ownership |
| Voice / diversity | Participant's own judgment in context | A novelty score from a single rater or detector |
| Relationship | Meaningful exchange and reflection content | Number of links or connections made |
| Access | Ability to participate with needed accommodations | Account creation or activation alone |
| Burden / wellbeing | Participant feedback on time and ability to stop | A clinical or medical assessment |
A learner records an environmental sound, compares their own observation with an AI-generated description, explains why they kept or rejected part of it, asks someone with local experience about what they heard, creates a short piece, and chooses how (or whether) to share it. Each step above maps to a different construct in the matrix — the scenario exists only to show which evidence would answer which question, not to describe anything that has happened.
Participation, Accessibility & Rights
参与、无障碍与权利
An original proposed design, informed by UNICEF's guidance [16] — not a description of anything currently implemented. Proposed design
Separate kinds of consent
Ordinary participation in a project, opting into research, granting permission to be recorded, and choosing to share publicly should be four distinct decisions. Declining research participation should never remove ordinary support or access to the activity itself.
Age-appropriate process
Clear, age-appropriate explanation and guardian permission where required; appropriate ethical or institutional review is to be determined through an actual process, not assumed or claimed as already obtained. Data collection should be minimized, stored privately with role-based access, carry a clear retention period and withdrawal process, remain reviewable by the participant, and never require public publication.
Accessible by default
Audio and text alternatives, captions and transcripts, keyboard navigation, low-bandwidth or offline drafting, a participant's preferred language, and a flexible pace should be built in from the start — not added after a pilot reveals a gap. Youth co-design should mean substantive control over questions, interpretation, and presentation, not only feedback collection after decisions are made. Rural residence should never be treated as a proxy for lower capability.
Keep costs and roles separate
Nonprofit-funded program and research costs should stay distinct from Kong Group's commercial development of EarthLinC. Any future service or cost arrangement needs its own actual, disclosed terms — it should never be assumed free, discounted, or already approved by virtue of appearing in this reference.
Claims & Readiness
可以怎样对外表述
What this Station's sources actually support, versus what remains unproven.
| Can say now | Conditional / proposed | Cannot yet say |
|---|---|---|
| HCAI overlaps with, rather than replaces, AI literacy and responsible AI (Section A). | Study may test whether a visible decision-and-revision trace improves measured independent transfer (Section I). | That any YuCROWN project improves learning, agency, or creativity. |
| Named bounded studies show mixed, context-specific effects of AI assistance (Section B). | EarthLinC could, in principle, record a fuller human-process chain than typical activity logs (Section E–F). | That YuCROWN's approach is unique, superior, or already validated. |
| Specific, dated funding conditions exist for Young Futures and Spencer, with named unresolved points (Section C). | A research-practice partnership route may open if a sustained partnership is built (Section C). | That any funder has endorsed, reviewed, or committed to YuCROWN. |
| Named standards (C2PA, PROV, Open Badges, Caliper/xAPI) are extensible and partially process-capable (Section E). | A consent-based, participant-controlled process record is a testable design target (Section F, J). | That a confirmed school partnership, ethics approval, or completed study exists. |
A funding-readiness evidence pack — what would still need to exist
A source-backed problem statement; a bounded, already-running program activity; documented youth co-design evidence; actual tested work and participant feedback collected with permission; any partner letters, but only if genuinely confirmed; clear role and cost responsibilities; a measurement plan; a governance description; and the specific funder's current eligibility, stage, and deadline. Internal data currently unknown or unverified should be listed honestly rather than estimated.
A proposed theory of change — framed as hypotheses, not outcomes
Interest or question → supported practice and real choices → explainable work and reflection → potential capability or connection → evidence review. Every arrow in this chain is an open hypothesis for Study to test, not a claim that any stage has been demonstrated.
Bibliography, Maintenance & Open Questions
来源与后续核查
This is a scoped review of selected primary sources, not a systematic or exhaustive literature review. All access dates below are 2026-10-03 unless noted.
| # | Source | Date | Type | Verification |
|---|---|---|---|---|
| 1 | UNESCO — AI Competency Framework for Students | 2024 (page updated Jan 2026) | Institutional framework | Verified |
| 2 | OECD — Digital Education Outlook 2026 | 2026-01-19 | Policy synthesis | Verified |
| 3 | Stanford HAI — About | Undated page | Institutional page | Verified (live, 2026-10-03) |
| 4 | Microsoft Research — People-Centric AI | Undated, maintained | Corporate research theme | Verified |
| 5 | Lee et al. — Impact of Generative AI on Critical Thinking | CHI 2025 | Peer-reviewed survey study | Verified |
| 6 | Bastani et al. — Generative AI Without Guardrails Can Harm Learning | PNAS, 2025 | Peer-reviewed RCT | Verified |
| 7 | Doshi & Hauser — Generative AI Enhances Individual Creativity | Science Advances, 2024-07-12 | Peer-reviewed RCT | Verified |
| 8 | Oreopoulos & Low — One Click Away (Khanmigo) | NBER WP 35620, 2026-08 | Working paper — non-peer-reviewed | Needs checking (full text 403) |
| 9 | Young Futures — Unwritten Challenge | Live, 2026 cycle | Funder program page | Verified (conditions) fit needs checking |
| 10 | Spencer Foundation — RPP grants | Live page | Funder program page | Verified (conditions) |
| 11 | Google.org — Knowledge, Skills & Learning | 2025-03 update cited | Corporate philanthropy page | Needs checking |
| 12 | Microsoft for Nonprofits | Live page | Corporate program page | Needs checking |
| 13 | FIRST / Brandeis longitudinal study | 10-year final report; publication date not audited here | Program + university summary | Needs checking (403 on detail page) |
| 14 | Khan Academy — efficacy overview | Live page | Organizational summary | Verified (existence) |
| 15 | Code.org / CodeAI — accessibility reporting | 2026-04 | Organizational disclosure | Verified |
| 16 | UNICEF — Policy Guidance on AI and Children v3 | 2025-12 | Policy guidance | Verified |
| 17 | C2PA Specification v2.4 | Live spec | Technical standard | Verified |
| 18 | W3C PROV-DM | 2013-04-30 | W3C Recommendation | Verified |
| 19 | 1EdTech Open Badges 3.0 | Live spec | Technical standard | Verified |
| 20 | Caliper Analytics 1.2 / xAPI data spec | Live specs | Technical standards | Verified |
| 21 | UNESCO — Living Heritage & Education | Undated | Institutional page | Needs checking (partial retrieval) |
Outstanding priorities for the next revision
Review the Young Futures full RFP before any eligibility or application decision; confirm overseas-delivery eligibility and resolve the January 2027 vs. April–August 2027 Academy-start conflict directly with the funder — do not rely on this reference's summary alone. Obtain and audit the full NBER w35620 text before citing any effect size. Retrieve and review the full FIRST/Brandeis methodology and publication date. Design actual instruments and seek appropriate ethical review with Cali before any data collection. Conduct a real technical capability audit of EarthLinC rather than relying on this reference's honest "needs checking" placeholders. Bring the canonical definitions of Earth Library, Conversations, and Study back into alignment across OS, the public organization Station, and Cali's notes, through human review — not through this Station unilaterally.
Refresh rule
Time-sensitive items (funding windows, program names, live page content) must be rechecked at the point of use, not assumed current from this reference. Primary research entries should be revisited on substantial revision of this Station, not on a fixed schedule — none is claimed here.
- Expanded source verification across definitions, evidence, funding, and standards; added a 21-entry bibliography with access dates and verification levels.
- Added bounded, source-specific empirical evidence (Section B) in place of general claims.
- Corrected the overstatement that HCAI replaces AI literacy/responsible AI, and the overstatement that no standard can represent reasoning.
- Added verified funding conditions, including an unresolved date conflict in Young Futures' own materials.
- Added proposed research questions and methods (Section I), participation/accessibility design (Section J), claims-and-readiness accounting (Section K), and this bibliography (Section L).
- Added a YuCROWN application map with an explicit, unresolved source-reconciliation note (Section G) — no other Station was edited.
- Cover illustration added to the hero slot (IPFS-hosted image).
This revision adds a complete Simplified Chinese translation and a functional Chinese/English language switch to this same Station. This is a localization and interface change only — no new research, sources, figures, or claims were added or altered. All substantive content, sources, and the 2026-10-03 (v2) research date remain as described above.
Research Watch
研究精选
A standalone, ongoing column serving YuCROWN Study (Human-Centered AI Learning & Research). This section is a standalone column and is not part of the research content of Sections A–L.
Column setup Five issues listed
- Column set up on
- 2026-10-18
- Issues listed
- 2026-10-18 (Issue 1), 2026-10-23 (Issue 2), 2026-10-30 (Issue 3), 2026-11-06 (Issue 4), 2026-11-13 (Issue 5)
- Next routine issue
- 2026-11-20; every Friday morning thereafter (Asia/Shanghai).
- Current status
- Listed: 2026-10-18, 2026-10-23, 2026-10-30, 2026-11-06, 2026-11-13; next routine issue: 2026-11-20.
- Focus
- Youth self-expression and judgment; authorship, control, and ownership of human creative work and personal experience; human–AI collaboration.
Selection standard and entry fields
Each issue selects 3–5 new papers, research reports, or high-quality scholarly analyses — quality over quantity. Each entry records: original title; author(s)/institution; publication date; category (peer-reviewed / preprint / report / opinion); original link and DOI where available; core findings; method and sample; strength of evidence and limitations; specific implications for Earth Library or Study; and questions young people can explore independently, with ways to start.
Original study conclusions are kept distinct from practical inferences. Entries based only on an abstract, or without a youth sample, are explicitly flagged.
De-duplication and history
Each issue's selections will be kept here by date. De-duplication checks against this Station's existing bibliography (Section L) using DOI, normalized title, and the relationship between preprint and published versions; an item is re-listed only when it has substantively new results or a changed publication status, and what is new is stated. Dates and verification statuses of earlier sources remain as they are.
2026-11-13 · Issue 5
Focus: work newly published or substantively updated 7–13 November 2026. Checked the current Station, Section L’s 21 base sources, the four previous issues and the 15 M-index entries by DOI, normalized title and versions. All three items are new to this Station; two full texts read, one abstract only. Search/crawl dates are not publication dates. “Agency” has different meanings across these papers. Felt authorship, psychological ownership, credit, release control and legal rights remain distinct; no copyright determination is made. All applications and youth questions are YuCROWN inferences / candidate explorations, not established program effects.
1. Generative AI use and scientific literacy among 15-year-olds in Beijing, Shanghai, Jiangsu, and Zhejiang, China: the mediating role of academic help-seeking Verified (full text)
- Authors / institution
- Bing Wu, Xianpeng Jiao, Changle Yang, Jie Wang, Qingshuang Dong; School of Communication, Qufu Normal University.
- Date / type
- 9 November 2026; Frontiers in Psychology, peer-reviewed Original Research; full text read.
- Source
- frontiersin.org — full text
- DOI
- 10.3389/fpsyg.2026.2013021
- Checked
- 13 November 2026
- Method / sample
- Secondary PISA 2025 analysis, 12,191 fifteen-year-olds in 374 schools; weighted two-level models, ten plausible values, school-fixed-effect and BRR checks. AI use, help-seeking and metacognition were self-reported; science was assessed.
- Finding
- Zero bivariate correlation between AI use and science literacy; in adjusted models, a negative direct association and a positive indirect association, including β=.013 via help-seeking. Help-seeking means a general orientation toward classmates/teachers, not chatbots; the sequence is unknown.
- Evidence
- Medium for association, low for causality. The youth sample and careful design-weight treatment help; cross-sectional data, no prior achievement, no preregistration, an equally compatible reverse model, small associations, weaker coverage of missing cases and four high-achieving Chinese regions limit inference. No AI benefit or harm, and no AI-induced human help-seeking, is established.
- Study implication
- YuCROWN inference / candidate exploration Note separately the amount of AI use, its purpose and any subsequent human help. Across Study’s question–observation–organization–expression–sharing–response process, take one unresolved AI-discussion question to a peer or teacher and revise the explanation independently. This workflow is untested here.
- Youth question / start
- Candidate exploration What still requires asking a real person? Use a boat or animal observation: write your own explanation, identify a gap, check one primary source and hear one response; private notes are fine. No authorship or ownership outcome was measured.
2. Perceived Agency During Agent–Avatar Control Transitions in Virtual Reality Verified (full text) Preprint · peer review unverified
- Authors / institution
- Jingyi Zhang, Anthony Steed; University College London.
- Date / type
- 9 November 2026; arXiv:2611.12529v1 preprint; peer review / formal venue unverified; full text read.
- Full text
- arxiv.org/html/2611.12529v1
- Metadata
- arxiv.org/abs/2611.12529
- Repository DOI
- 10.48550/arXiv.2611.12529 (DataCite registration pending per arXiv; not a formal conference DOI)
- Checked
- 13 November 2026
- Method / sample
- 24 adults aged 18+, modal age 25–29; 75-minute within-subject VR study with four counterbalanced live/prerecorded controller sequences (always prerecorded, prerecorded to live, live to prerecorded, always live) and fixed five-question interviews. “Computer” behavior was Wizard-of-Oz prerecorded human speech/movement, not generative AI. Data: questionnaires, retrospective slider ratings and interviews.
- Finding
- No significant perceived-controller updating after transitions; initial disclosure and initial behavior jointly mattered, so the label alone cannot be credited. Later live control was more liked. “Perceived agency” means belief about the virtual character’s controller, not the participant’s own autonomy.
- Evidence
- Low–medium for this task. Counterbalancing helps; a small adult sample, scripted task, retrospective/self-report measures and live/prerecorded differences limit inference. Nonsignificance is not proof of no updating. No youth, actual generative AI, long-term relationship or ownership claim.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration When showing others’ experience or AI-assisted responses, distinguish experience-provider, author, AI-assisted editor and current respondent rather than leaving readers to guess from human-like presentation. Small trial: add an optional contribution note to one consented excerpt and ask a peer whose words they think they are reading; use authorized material, no deception, no voice or likeness cloning.
- Youth question / start
- Candidate exploration Do I infer a human response from content, expression or disclosure? Compare the original and a clearly labeled AI-assisted revision of one’s own work; record the cues and ask the author about accuracy. Human-like feeling is not evidence of lived experience or permission.
3. AI and the responsibility-bearing status of authorship Abstract only · full text needs checking
- Author / institution
- Caterina Moruzzi; University of Edinburgh, Edinburgh College of Art / Design Informatics.
- Date / type
- Institutional record: 12 November 2026 Accepted / In press; Philosophy & Technology, peer-reviewed accepted philosophical/conceptual analysis; not empirical research and not a verified final-issue publication.
- Institutional source
- research.ed.ac.uk — institutional record
- DOI
- No verifiable article DOI is listed in the institutional record; none is invented here.
- Checked
- 13 November 2026 (abstract only)
- Reading boundary
- Institutional abstract and metadata only; the accepted manuscript is marked embargoed. Full legal scope, case selection, counterarguments and boundaries are unverified.
- Abstract-level claim
- Reconstructs copyright authorship as a responsibility-bearing status involving intentional agency, guided control and relational expression (ACE), addressing human AI-assisted authorship and possible authorship gaps for partly generated output. This is a theoretical reconstruction, not a court ruling, new law or a felt-ownership measure.
- Method / object
- Philosophical/conceptual argument; no empirical sample or intervention; the abstract does not specify complete legal materials or analytic procedure; no youth data.
- Evidence
- Empirical strength not applicable; the argument cannot be fully assessed from the abstract. Acceptance does not validate effects or establish applicability across jurisdictions; no legal ownership determination for any Earth Library work follows.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration State intention, actual choices, source of expression and release responsibility separately. Small trial: creators may optionally write three sentences — what I wanted to express / what decisions I made / which part I take responsibility for — with sharing permission expressed separately. This is not a copyright test and yields no copyright conclusion.
- Youth question / start
- Candidate exploration When do willingness to credit, felt authorship and willingness to take responsibility diverge? Answer each separately for a non-sensitive small work; any label, or publication, may be declined.
Asking people for help need not mean abandoning judgment; perceived human presence does not establish a live human response; credit, felt authorship and legal rights do not substitute for each other. Study (Human-Centered AI Learning & Research) may optionally retain a question, a trade-off, a verified source, an independent explanation and a real response. Earth Library’s voluntarily shared works and private process are not default research data.
Issue DOI / title de-duplication list
- 10.3389/fpsyg.2026.2013021 — Generative AI use and scientific literacy among 15-year-olds in Beijing, Shanghai, Jiangsu, and Zhejiang, China: the mediating role of academic help-seeking — formally published 2026-11-09, full text checked 2026-11-13, new.
- 10.48550/arXiv.2611.12529 — Perceived Agency During Agent–Avatar Control Transitions in Virtual Reality — v1, 2026-11-09; a later conference version of the same study must be merged as a version update.
- No verifiable DOI — AI and the responsibility-bearing status of authorship — Moruzzi, accepted / in press 2026-11-12, abstract only; de-duplicated by normalized title + author + institutional URL; full publication must be recorded as a version update.
2026-11-06 · Issue 4
Focus window: 31 October–6 November 2026. De-duplication used the current full Station, Section L’s 21 base sources, the three previous issues and the 12-entry M index, matching DOI, normalized titles and versions. Three new items below, plus one same-study publication update; all full texts read. arXiv inclusion or a conference listing alone does not establish verified peer review. Agency, felt authorship and control are distinct from legal ownership; no copyright determination is made. All proposed applications and youth questions are YuCROWN inferences, untested by these papers.
1. Who Is Doing the Thinking? Characterising Human Agency in First-Year Students’ AI Interactions Verified (full text) Preprint · peer review unverified
- Authors / institution
- Ali Shakiba, Thomas Chaffey; University of Sydney.
- Date / category
- 3 November 2026; arXiv:2611.04783v1, preprint; peer review unverified. Ignore the manuscript’s conference-template date and placeholder DOI.
- Full text
- arxiv.org/html/2611.04783v1
- Metadata
- arxiv.org/abs/2611.04783
- Repository DOI
- 10.48550/arXiv.2611.04783 (registration pending according to arXiv; not a journal DOI)
- Checked
- 6 November 2026
- Findings / method
- 962 self-reported prompts from 105 first-year software-engineering students over ten weeks; 27 sustained contributors formed a longitudinal subset. HADT codes visible human engineering contribution in prompts: 84.2% exceeded pure delegation; profiles varied with project phase. It measures neither learning nor a quality ladder.
- Evidence strength
- Low–medium, descriptive. Double-coding and student-cluster checks help; one course, selective reporting/attrition, instructor-coders, missing conversation context/attachments and uncorrected multiple testing limit inference. No causality; ages unspecified, no established minor sample.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Voluntarily retain one decision note per Study phase (question–observation–organizing–expression), stating what judgment preceded AI, rather than scoring prompt length or product complexity.
- Youth questions / start
- Candidate exploration Which prompt content came from my observation? In three interactions, mark observation, judgment and delegation; explain once after closing AI. No ranking of people.
2. Measuring the collaboration, not the agent Verified (full text) Preprint · peer review unverified
- Expanded full-text title
- Measuring the Collaboration, Not the Agent: An Evaluation Framework for Human–AI High-Dimensional Visual Analytics
- Authors / institutions
- Xinxiao Li, Shonan Institute of Technology; Eisei Nakahara, Nihon University.
- Date / category
- 4 November 2026; arXiv:2611.06487v1, preprint / framework and simulation. The official IEEE VIS VISxGenAI program lists the 9 November workshop; formal peer review or journal publication is not independently established.
- Full text
- arxiv.org/html/2611.06487v1
- Metadata
- arxiv.org/abs/2611.06487
- Program
- rio.ieeevis.org — VIS 2026 program page
- Repository DOI
- 10.48550/arXiv.2611.06487 (registration pending)
- Checked
- 6 November 2026
- Findings / method
- Proposes five axes: complementarity, human intervention cost, appropriate reliance, retained human feedback across sessions, and agency. SECOM’s 1,567 semiconductor production records and a simulated analyst calibrate the first four axes; no human participants. Retention means agent memory of feedback, not student learning.
- Evidence strength
- Human-effect strength not applicable; low–medium as instrument calibration. Proxy ground truth and weight/policy sensitivity; the agency axis is untested. No youth, learning, real collaboration benefit or ownership is established.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Compare solo-human, AI-only and collaborative small explanations, recording revision burden alongside quality, without importing the industrial scoring formula.
- Youth questions / start
- Candidate exploration What did one extra judgment improve? Note reasons for three accept/reject/revise choices; ask a peer to look blind and say which revision is clearer. Sharing the process remains optional.
3. CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances Verified (full text) Preprint / accepted conference manuscript · review unverified
- Authors / institutions
- Torin Hopkins, Shih-Yu Ma, Zander Trafton-Hancock, Ellen Yi-Luen Do, Luca Turchet; SolJAMM/University of Colorado Boulder and University of Trento.
- Date / category
- 2 November 2026; arXiv:2611.03389v1 open manuscript. The author website states IEEE IS2 2026 Accepted; the review procedure and final conference DOI are not independently verified. Classified conservatively as preprint / accepted conference manuscript.
- Full text
- arxiv.org/html/2611.03389v1
- Metadata
- arxiv.org/abs/2611.03389
- Author source
- torinhopkins.com/publications/crowdforge
- Repository DOI
- 10.48550/arXiv.2611.03389 (registration pending)
- Checked
- 6 November 2026
- Findings / method
- Two live performances, 19 audience members (mean age 28.89), two musicians; audio, visual and combined conditions. Questionnaire n=14, behavioral logs n=17, interviews n=6. Overall agency did not differ significantly across modes (p=.721). Observable consequences were a qualitative/exploratory explanation, not a demonstrated causal mechanism.
- Evidence strength
- Low. Authentic mixed methods, but small uneven groups, performance/condition confounding, exploratory tests and self-report; age range unspecified, no established minor sample. Participation, felt control and social connection are separate outcomes.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Let creators optionally show one choice and its identifiable consequence rather than add more controls; ask separately about human connection.
- Youth questions / start
- Candidate exploration Does seeing my influence strengthen felt authorship? Compare two revisions of one small work; record visible effect, felt authorship and another person’s response separately. This does not establish legal ownership.
Generative AI configurations across creative phases: a 2 × 2 experiment on creative performance and immediate knowledge in art and design education
Yiming Wei, Sijun Li, Xi’an Academy of Fine Arts; DOI 10.3389/fpsyg.2026.1971021.
Issue 2 checked only the abstract on 23 October. The peer-reviewed Frontiers in Psychology Original Research article was formally published on 4 November; full text read on 6 November: frontiersin.org — full text.
Newly verified: 120 art/design students aged 18+, randomized into four groups of 30; Tang sancai camel colour reconstruction; three blind expert raters and an immediate knowledge test. Evidence is medium for the implemented task/configurations only; modality, interaction and interface are bundled; the test lacked independent pre-experiment validation, and later validation does not replace it; no long-term transfer.
Explicit correction to Issue 2: the image-configuration aesthetic advantage in the convergent phase became non-significant after Holm correction, not “without” correction as the Issue 2 abstract-based wording ambiguously stated. The historical wording is preserved; this note corrects it. Text→Image showed no overall creativity advantage; higher immediate knowledge is not durable learning. No new experiment or sample; no duplicate count.
YuCROWN trial: separate product rating from unaided explanation, without prescribing a universal sequence.
Youth question / start: Does text-then-image help understanding or completion? Try a small task and explain your reasons aloud for 60 seconds after closing AI; an exploration, not a validated minor intervention.
For Study (Human-Centered AI Learning & Research), visible process, felt control, independent capability, collaboration quality and human response are distinct. Logs are not thought itself; simulation is not human-effect evidence; acceptance is not strong evidence. Across the existing Study process, a creator may optionally keep a question, a trade-off, an independent explanation and a real response; publication remains the creator’s choice.
Issue de-duplication list
- 10.48550/arXiv.2611.04783 — Who Is Doing the Thinking? Characterising Human Agency in First-Year Students’ AI Interactions — v1, 2026-11-03, new.
- 10.48550/arXiv.2611.06487 — Measuring the collaboration, not the agent (full-text/workshop expanded title is the same study) — v1, 2026-11-04, new.
- 10.48550/arXiv.2611.03389 — CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances — v1, 2026-11-02, the author conference manuscript is the same study.
- 10.3389/fpsyg.2026.1971021 — Generative AI configurations across creative phases: a 2 × 2 experiment on creative performance and immediate knowledge in art and design education — same study as Issue 2; formally published 4 November, full text verified 6 November.
2026-10-30 · Issue 3
Theme: distinguish the ability to evaluate an output, question a system, decide which cognitive function to delegate, monitor one’s own thinking, and revise generic advice through situated experience. This issue checked work newly published or substantively updated from 24–30 October 2026 and de-duplicated it against Sections A–L, Issue 1 (18 October), and Issue 2 (23 October) by DOI, normalized title, and preprint/published-version relationships. The four entries are new, non-duplicate, peer-reviewed studies with full text read. Only Item 1 directly includes minors; transfer from Items 2–4 to youth settings is limited. “Sense of authorship/control/psychological ownership” refers to subjective experience and is not copyright or other legal ownership. None of these studies determines legal rights. All Earth Library / Study implications and youth explorations are YuCROWN inferences, not original research findings.
1. Student epistemic agency in AI-mediated practices Verified (full text)
- Author / institution
- Nina Bergdahl; Halmstad University, Sweden.
- Type / status
- Peer-reviewed Research article, Smart Learning Environments; published online 24 October 2026, Version of Record 26 October 2026; full text checked.
- Source
- link.springer.com/article/10.1186/s40561-026-00464-y
- DOI
- 10.1186/s40561-026-00464-y
- Checked
- 30 October 2026
- Core finding
- Output-directed epistemic agency was stronger than system-directed agency. Of 47 students, 35 scored higher on output- than system-directed agency, four showed the reverse, and eight were equal; exact sign test p<.001. Seventy-nine per cent varied by at least three response levels across nine scenarios. Students were relatively stronger on source evaluation, responsibility, and governance, but weaker on privacy and algorithmic influence: 66% chose the lowest-agency response for privacy and 47% for algorithmic influence. One student treated changing an AI answer as necessary to call a game their own; this is a clue about felt authorship, not legal rights.
- Method / sample / context
- Exploratory mixed-methods case study in one Swedish compulsory school; 47 Year 7 students and one teacher; weekly 90-minute AI sessions using school laptops and Gemini Pro. End-of-term researcher-designed nine-scenario survey with ordered A–D responses and written reasons, plus classroom observation; written guardian consent.
- Evidence strength
- Low–medium. Direct youth evidence in an authentic classroom and multiple data types, but one school, n=47, no independently validated instrument, single-author qualitative coding, observations not linked to individual surveys, no control or longitudinal design. Descriptive/associational; no causal effect of the course and limited transfer across ages, cultures, or tools.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Record “can evaluate an output” separately from “can question data, privacy, defaults, and incentives.” Small trial: verify sources for one answer, then map what was entered, who may store it, and who is affected; compare the two kinds of agency.
- Youth questions / start
- Candidate exploration (a) Can I reject a plausible answer on an unfamiliar topic? Ask one familiar and one unfamiliar question and record verification paths. (b) What kind and amount of change makes a work feel mine? Keep one accepted, rejected, and rewritten choice; record felt authorship and control separately without inferring legal ownership.
2. Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy Verified (full text)
- Authors / institutions
- Wei Xiao, Cheng-Hao Men, Jia-Cong Zhang; Shanghai Normal University and Shandong University.
- Type / status
- Peer-reviewed Original Research, Frontiers in Psychology; published 28 October 2026; full text checked.
- Source
- frontiersin.org — full text
- DOI
- 10.3389/fpsyg.2026.1975833
- Checked
- 30 October 2026
- Core finding
- Across four online experiments, higher AI assistance or a high-delegation workflow increased cognitive offloading and metacognitive disengagement and, in one task, reduced expert-rated knowledge-innovation quality. Experiment 1: F(1,144)=16.85, p<.001, η²=.105 for disengagement. Experiment 2: higher assistance increased disengagement (d=.71) and offloading (d=1.08), but offloading was measured, so mediation direction is not fully established. Experiment 3: high delegation produced lower expert-rated quality (M=3.95 vs 4.78, d=.75) and more disengagement (d=.95). Experiment 4: the negative indirect effect was stronger on complex tasks and weaker with higher AI literacy; the higher-literacy confidence interval included zero.
- Method / sample / context
- Four independent Credamo experiments with Chinese adults, total N=728: n=150 smart-home innovation; n=162 community-service innovation; n=176 urban public-space renewal; n=240 2×2 offloading×complexity experiment. Mean age was about 29 in the first three. Three blind experts rated novelty, usefulness, and developability in Experiment 3, ICC=.84.
- Evidence strength
- Medium for short-term adult workflows. Randomized manipulations support some short-term causal claims and one experiment used expert outcomes. Limits: the high-delegation workflow bundled detail, effort, and responsibility; the complete sequential mechanism was not tested in one sample; new self-report measure, short online tasks, no minors, no long-term learning. Youth transfer is limited.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Preserve small visible traces of planning, monitoring, and reflection. Small trial: do one research/creative task twice, once with a full AI solution and once with factual hints only; compare output and ability to name an assumption, rejected option, and next step.
- Youth questions / start
- Candidate exploration (a) Which AI help frees attention and which removes thinking? Label uses as memory, organization, or judgment and add one reason. (b) Does complexity make fluent answers more likely to stop checking? Try one familiar and one unfamiliar problem, then explain the reasoning after closing AI.
3. Development and validation of the AI cognitive offloading scale for Chinese college students Verified (full text)
- Author / institution
- Hongying Chang; School of Educational Sciences, Minzu Normal University of Xingyi, China.
- Type / status
- Peer-reviewed Original Research, Frontiers in Psychology; published 27 October 2026; full text checked.
- Source
- frontiersin.org — full text
- DOI
- 10.3389/fpsyg.2026.1884676
- Checked
- 30 October 2026
- Core finding
- A 20-item scale distinguishes memory, assistive, and thinking offloading. The three-factor structure was confirmed in an independent sample, with reported internal consistency, convergent/criterion validity, and gender invariance. All dimensions correlated positively with AI dependence and negatively with metacognitive monitoring; total and assistive offloading correlated negatively with need for cognition, while memory and thinking offloading individually did not. The scale measures self-reported tendencies, not agency, learning, or creative outcomes; correlations are not causal.
- Method / sample / context
- Study 1: online convenience sample of Chinese university students, n=665, for item analysis and EFA. Study 2: independent convenience sample, n=1,123, for CFA, reliability, validity, and invariance. Criteria included AI dependence, need for cognition, and metacognitive monitoring.
- Evidence strength
- Medium as initial measurement development; low for claims about effects. Strengths are large independent EFA/CFA samples. Limits are convenience, self-report, cross-sectional data, highly related factors needing stricter discriminant checks, one national/college context, and no minors. Age and cultural transfer are limited.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Do not compress every AI use into one “dependence” score. Ask which function was delegated: memory/storage, organization/execution, or judgment/decision. Small trial: an optional three-question diary for those functions, without diagnosis or ranking.
- Youth questions / start
- Candidate exploration (a) Which function do I delegate most? Label five uses M, A, or J. (b) After delegating judgment, can I still explain and disagree? Write a decision before seeing one AI recommendation, then record whether and why it changed.
4. Catalysts, Not Only Constraints: Pre-Service EFL Teacher Agency in Generative AI-Assisted Lesson Planning Verified (full text)
- Authors / institutions
- Cihat Atar, Yildiz Technical University; Serdar Tekin, Nevsehir Haci Bektas Veli University; Türkiye.
- Type / status
- Peer-reviewed original qualitative case study, Behavioral Sciences 16(11):1998; published 26 October 2026; full text checked.
- Source
- mdpi.com/2076-328X/16/11/1998
- DOI
- 10.3390/bs16111998
- Checked
- 30 October 2026
- Core finding
- When generative AI gave generic, clichéd, or impractical suggestions, pre-service teachers exercised agency by recontextualizing content, substituting/rejecting suggestions, and improvising in class. All 58 changed at least some AI plans; none abandoned all tools. Participant-by-tool reports included 90 context gaps, 68 clichéd activities, 61 unsuitable/boring suggestions, and 45 timing problems. These are descriptive counts, not comparative effects.
- Method / sample / context
- 58 final-year pre-service EFL teachers, ages 21–27, at one Turkish state university. Each planned with ChatGPT GPT-4o, Gemini 2 Flash, and DeepSeek-V3 and taught in practicum schools. Data: 58 reflective essays and observations of 13 lessons. Placements included 33 high-school and 25 middle-school settings, but no school-student data were collected.
- Evidence strength
- Low–medium, descriptive. Authentic planning-to-teaching and partial triangulation are strengths. Limits: one convenience cohort, only 13 observed lessons, graded reflective assignment with prompts about changes, possible social-desirability/priming, self-selected tools/topics/order not recorded, no valid tool comparison, and no student outcome. Adult participants; no direct youth inference.
- Earth Library / Study implication
- YuCROWN inference / candidate exploration Use AI’s missing context as a judgment exercise: identify what does not fit one’s people, place, or experience, then keep, adapt, reject, or improvise. Small trial: youth mark one keep/adapt/reject in a generic AI description of a local observation and explain why; only the voluntarily shared final work is presented.
- Youth questions / start
- Candidate exploration (a) Which details can only someone here supply? Underline three generic lines and replace one with an observation. (b) Does rejecting an AI suggestion show authorship more than prompt fluency? Save one rejected suggestion and reason; record felt authorship separately from legal ownership.
Output-level judgment, system-level questioning, cognitive-function delegation, metacognitive engagement, and situated revision are related but distinct dimensions of agency. Only Item 1 directly supports a Year 7 context; the others are hypotheses for youth exploration. Study can retain a few decision traces rather than treat amount of AI use as one proxy. Earth Library can let creators optionally attach what they kept, changed, or rejected when voluntarily sharing a final work, without requiring disclosure of private process.
Owning what one did not make: psychological ownership, agency, and the authorship legitimation deficit in human–AI co-creation (Dong et al.; DOI 10.3389/fpsyg.2026.1928186) was included in Issue 2 as a Conceptual Analysis accepted on 19 October 2026 with only the abstract available. The full version was published 28 October and read on 30 October. It remains a conceptual analysis with no sample or new empirical result, so it is not recommended again. Full text: frontiersin.org — full text. Its original Issue 2 listing and checked date are preserved; the update is publication status and full-text availability.
Issue de-duplication list
- 10.1186/s40561-026-00464-y — Student epistemic agency in AI-mediated practices
- 10.3389/fpsyg.2026.1975833 — Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy
- 10.3389/fpsyg.2026.1884676 — Development and validation of the AI cognitive offloading scale for Chinese college students
- 10.3390/bs16111998 — Catalysts, Not Only Constraints: Pre-Service EFL Teacher Agency in Generative AI-Assisted Lesson Planning
2026-10-23 · Issue 2
Theme of Issue 2: technical vocabulary, iterative control, phase-specific performance, and authorship/responsibility roles are different variables and need to be observed separately. "Psychological ownership" and "sense of authorship" below refer to subjective experience in specific studies or a conceptual analysis; they are not legal ownership judgments.
Search window: we focused on checking content newly published or substantively updated between 2026-10-19 and 2026-10-23, and de-duplicated it one by one by DOI, normalized title, and version relationship against Sections A–L of this page, the 21 base sources, and the 4 items of the 2026-10-18 first issue; the 4 items below have no duplicates. In this issue, 2 items have full text (items 1 and 2) and 2 currently have the abstract only (items 3 and 4) — the full text of the latter two was not read, and their methods, measures, and argument details are unverified. No older papers were used as filler. This issue has no sample of minors. Every "implication/exploration" is a YuCROWN inference / candidate exploration, not a finding of the original study.
1. Prompt specificity and iterative behaviour in text-to-music co-creation: a secondary analysis of interaction logs and a framework for perceived creative control Verified (full text)
- Authors
- Xian Zhang, Lu Pan (Xihua University; Shenyang Conservatory of Music)
- Type / status
- Peer-reviewed Original Research, Frontiers in Psychology; formally published 2026-10-21; full text verified.
- DOI
- 10.3389/fpsyg.2026.1965465
- Accessed
- 2026-10-23
- Design
- Secondary analysis of published PAGURI logs: 304 text-to-music prompts from 24 participants (original-study mean age 26.9, mostly Italian, relatively high musical engagement, limited prior text-to-music experience); grouped by self-reported role as producer/technical n=6, performer n=10, non-performer/other n=8. Technical/instrument specificity was computed with lexicon tagging; iteration was measured by Jaccard similarity of consecutive prompts, with a TF-IDF cosine robustness check; inference used participant-level summaries so the 304 prompts were not treated as independent samples.
- Bounded finding
- Participants grouped by self-reported role differed clearly in prompt technical/instrument specificity (participant-level mean specificity H(2)=10.25, p=.006, ε²=.39; specificity density H(2)=11.52, p=.003, ε²=.45), but refinement rate did not differ between groups (H(2)=.24, p=.888); nor did prompt specificity rise systematically within a session (mean within-person Spearman ρ=-.06; W=89.0, p=.374). This suggests that "being able to use more specialist terms" and "being able to keep advancing one's own intent" are two different things.
- Evidence / limitation
- Evidence: low–moderate. Strengths: public corpus, recomputable, participant-level statistics, and threshold-sensitivity analysis. Limitations: n=24; role is not a validated expertise scale; a single system and roughly one-hour sessions; lexicon and surface similarity are not valid direct measures of creative intent or sense of control; no learning outcomes. No causal conclusion can be drawn, and it cannot be extrapolated to minors.
- Implication
- YuCROWN inference / candidate exploration When recording AI collaboration, record separately "can I say precisely what I want" and "can I keep and advance a direction." Small trial: in one 15-minute sound/image/text experiment, a young person first writes one sentence of intent, marks each round "go deeper" or "change direction," and finally points out which round still feels most like their own choice; no publication required, sharing is up to the person.
- Youth exploration
- Candidate exploration a. With more technical terms, does the work really get closer to what I meant, or just closer to language the tool recognizes? Start: write one everyday-language prompt and one technical-term prompt for the same intent, then listen/look blind and write down the differences. b. When is "starting over" giving up control, and when is it an active judgment? Start: keep a record of 3 rounds, writing just one sentence of reason each time you change direction.
- Source
- frontiersin.org — full text
2. A diagnostic human agency framework for AI driven education from a PRISMA guided systematic review Verified (full text)
- Authors
- Kandarp Acharya, Mili Gondaliya
- Type / status
- Peer-reviewed Review / conceptual framework, Discover Education 5, article 1206; formally published 2026-10-21; open full text verified.
- DOI
- 10.1007/s44217-026-02173-7
- Accessed
- 2026-10-23
- Design
- PRISMA-guided search, 2018-01 to 2026-03; OpenAlex + UNESCO/OECD/EU repositories + citation tracking. 2,910 records, 2,255 after de-duplication, 35 items in the final qualitative synthesis; 62 codes developed through open/axial coding into a four-dimension framework. Five practitioners (2 K–12 teachers, 1 corporate learning-and-development manager, 1 ed-tech developer, 1 national skills-agency policy adviser) reviewed and revised it; two cases serve only as illustrative applications. Coding agreement Cohen’s κ=.83.
- Bounded finding
- The authors synthesize agency in AI-driven education into four interrelated diagnostic dimensions: Purpose (what valuable capabilities learners can gain and pursue), Control (who controls path, data, and the right to exit), Collaboration (whether AI enhances or replaces human-to-human collaboration), and Conditions (structural conditions such as privacy, equity, and long-term support). The authors state explicitly that the framework (HAF) currently has conceptual validity only and has not been empirically validated in real settings.
- Evidence / limitation
- Evidence: moderate (as a conceptual map), low (as evidence of effects). Strengths: transparent search process, cross-source synthesis, coding agreement, and practitioner review. Limitations: only 35 items, no formal quality appraisal, English-language literature only, mainly Western/Global North material, only 5 practitioners, cases are not effect evaluations, and HAF is not validated.
- Note
- "Control" here covers path, data, exit, and deletion; it is not legal copyright. The paper also does not measure subjective sense of authorship.
- Implication
- YuCROWN inference / candidate exploration The four dimensions could be condensed into pre-publication self-questions rather than a scoring tool: whose purpose does this work express? Who can change/withdraw/delete the data? Has AI crowded out real responses? Are the conditions for participation fair and sustainable? Small trial: pick one voluntarily shared entry and have the creator write one sentence for each of the four questions; if they prefer not to publish, the record stays with them alone.
- Youth exploration
- Candidate exploration a. Which "choices" really change the process, and which are only fake choices in the interface? Start: list every button/decision in one AI session and circle the three that change the goal, the content, or the audience. b. When AI helps more, do human responses decrease? Start: in one week, compare one "AI responds first" and one "peer responds first," noting only what you changed afterward.
- Source
- link.springer.com — open full text
3. Owning What One Did Not Make: Psychological Ownership, Agency, and the Authorship Legitimation Deficit in Human–AI Co-Creation Abstract only · full text needs checking
- Authors
- Yanjiao Dong, Ziran Zhou, Yueh-Chih Hsiao, Qi Cao (Anyang Normal University; National University of Singapore; National Taiwan Normal University; Hanyang University)
- Type / status
- Conceptual Analysis accepted after peer review, Frontiers in Psychology; accepted 2026-10-19. The Frontiers page states "final, formatted version will be published soon"; only the abstract was verified, so this is not marked as a formal version with full text available.
- DOI
- 10.3389/fpsyg.2026.1928186
- Accessed
- 2026-10-23 (abstract only)
- Design
- Conceptual analysis; the abstract mentions historical precedents of commissioned/distributed making and a role-relative taxonomy; no participants, no experiment. Because the full text is not yet public, the selection of material, analytic steps, and the boundaries of the full argument cannot be verified.
- Core claim
- The authors break authorship into at least three relations: experiencing the work as "mine," feeling oneself to be the agent of the action, and taking responsibility for publication and consequences; and they propose an "authorship legitimation deficit," where an authorship title exceeds what a given field expects as experiential or contributory grounds. Direction-setting, selection, editing, and publication decisions may also support some authorial role, so judgment should not rest only on initial production labor.
- Evidence / limitation
- Empirical strength: not applicable; conceptual value moderate, certainty of conclusions low. Peer review does not turn a conceptual proposition into an empirical effect; its propositions remain to be tested.
- Note
- Psychological ownership / felt ownership is subjective experience; it is not copyright or any other legal ownership.
- Implication
- YuCROWN inference / candidate exploration A work page could record separately "which decisions I made," "which part I am willing to put my name to / be responsible for," and "whether I am willing to make it public," rather than letting one author label swallow all these relations. Small trial: creators may choose a more accurate role label, such as "original-experience provider / director / editor / publication decider," or decline a label.
- Youth exploration
- Candidate exploration a. If a work is mostly executed by AI but the theme, trade-offs, and decision to publish came from you, how would you want to credit it? Start: write 3 different credit lines for the same work and explain which one feels least comfortable. b. In what situations do "this is mine" and "I am willing to be responsible for it" come apart? Start: record them on two separate 1–5 scales; keeping them private is fine.
- Source
- frontiersin.org — abstract
- Version update
- The final full text was published on 2026-10-28 and verified on 2026-10-30 (see the Issue 3 "Version update"); it remains a conceptual analysis. This entry's original listing and checked date (2026-10-23, abstract only) are unchanged. frontiersin.org — full text
4. Generative AI Configurations Across Creative Phases: A 2 × 2 Experiment on Creative Performance and Immediate Knowledge in Art and Design Education Abstract only · full text needs checking
- Authors
- Yiming Wei, Sijun Li (Xi’an Academy of Fine Arts)
- Type / status
- Original Research accepted after peer review, Frontiers in Psychology; accepted 2026-10-20. The Frontiers page states that the final typeset version will be published; only the abstract was verified.
- DOI
- 10.3389/fpsyg.2026.1971021
- Accessed
- 2026-10-23 (abstract only)
- Design
- 2×2 between-subjects experiment, N=120 art and design students; a standardized cultural-heritage color-reconstruction task, measuring creative performance, immediate task knowledge, and human–AI interaction process. The abstract does not give recruitment, age, how randomization was implemented, measurement instruments, blinding, effect sizes, or the full statistical plan — all unverified.
- Bounded finding
- Students were assigned to Text→Text, Text→Image, Image→Text, and Image→Image sequences. Text-dialogue AI produced higher creative performance than image-generation AI in the divergent phase, still significant after Holm correction; the aesthetic-quality difference for image generation in the convergent phase was no longer significant without family-wise error correction. The pre-specified Text→Image sequence did not improve overall creative performance (contrast=.066, p=.671; interaction p=.541), although immediate knowledge scores were higher. Phase-specific advantages did not automatically accumulate into a better final work.
- Evidence / limitation
- Evidence: provisionally low–moderate. The experimental structure and multiple-comparison correction are strengths, but with only the abstract available, key implementation and measurement details are unknown; single task, immediate outcomes, art and design students — not long-term learning, and not extrapolable to minors.
- Note
- The study did not measure legal ownership; the abstract also reports no subjective sense of authorship.
- Implication
- YuCROWN inference / candidate exploration Observe AI placed at different learning phases separately, without assuming "text first, then image" is a universally best sequence. Small trial: for the same small task, use path A "write two directions yourself → AI asks follow-up questions → make it yourself" and path B "AI produces an image first → you select and modify," recording for each the number of new ideas, the final reasons, and whether you can explain independently after leaving AI.
- Youth exploration
- Candidate exploration a. At which phase does AI help you widen the possibilities, and at which does it make you converge too early? Start: split the process into exploring/choosing, and write one "what AI helped with, what it blocked" for each. b. When the final works are similar, which path lets you better explain your decisions? Start: after finishing, switch AI off and spend 60 seconds telling three key trade-offs aloud.
- Source
- frontiersin.org — abstract
- Version update (Issue 4)
- Formally published on 2026-11-04 and full text verified on 2026-11-06; see the formal full-text update in Issue 4. This entry's original listing and checked date (2026-10-23, abstract only) are unchanged.
What the original studies jointly suggest: technical language, iterative control, phase-specific performance, and subjective authorship/responsibility roles are not the same variable; being better at one phase or on one indicator does not mean stronger overall learning, long-term capability, or sense of authorship.
YuCROWN inference: Study (Human-Centered AI Learning & Research) can keep using "pose a question — record observations — organize findings — express — bring to others — hear the response," while adding two lightweight records to each step: "Who made the irreplaceable decision in this step?" and "Can I explain and continue without AI?" This is only a design hypothesis to be tested, not proven effectiveness.
Keep columns separate: legal ownership, subjective psychological ownership, credit role, and publication control must be kept in separate columns and must not substitute for one another.
Scope: this issue has no sample of minors; any extrapolation to youth, especially minors, is limited. Items 3 and 4 were verified from abstracts only, and their conclusions await full-text checking.
DOI / title list for this issue
- Prompt specificity and iterative behaviour in text-to-music co-creation: a secondary analysis of interaction logs and a framework for perceived creative control — 10.3389/fpsyg.2026.1965465 (full text)
- A diagnostic human agency framework for AI driven education from a PRISMA guided systematic review — 10.1007/s44217-026-02173-7 (full text)
- Owning What One Did Not Make: Psychological Ownership, Agency, and the Authorship Legitimation Deficit in Human–AI Co-Creation — 10.3389/fpsyg.2026.1928186 (abstract only)
- Generative AI Configurations Across Creative Phases: A 2 × 2 Experiment on Creative Performance and Immediate Knowledge in Art and Design Education — 10.3389/fpsyg.2026.1971021 (abstract only)
2026-10-18 · Issue 1
Theme of the first issue: a more polished work, feeling more capable, and keeping one's own expression need to be observed separately. The creativity and ownership measures below are measures within specific studies and are not legal ownership judgments.
Main search window 2026-09-18 to 2026-10-18; this is a scoped selection, not a systematic review. All 4 items were read in full text (4/4), publication status was verified, and they were checked against Sections A–L and M of this Station with no duplicates found (no earlier issues exist). No opinion pieces or non-peer-reviewed preprints are included, and no type quota was padded. University/adult samples cannot stand in for minors. Every "implication/exploration" is a YuCROWN inference / candidate exploration, not a finding of the original study.
1. Drawing the Line: Where AI Guidance and Human Creativity Meet in Emotion-Driven Comic Storyboarding Verified
- Authors
- Jocelyn Shen, Isabella Pu et al. (MIT/Media Lab)
- Type / status
- Peer-reviewed conference study, ACII 2026; conference 8–10 September; open manuscript 2026-10-06 (arXiv v1). Conference date and repository date differ. The open full text was read, not only the abstract.
- ID / DOI
- arXiv:2610.09155v1 · 10.48550/arXiv.2610.09155 (repository DOI, not an IEEE publication DOI)
- Accessed
- 2026-10-18
- Design
- 25 non-professional creators aged 18–29; counterbalanced within-subject comparison, drawing different personal stories with EmoToon and by hand.
- Bounded finding
- Questionnaires and interviews: with AI assistance, self-rated emotional expression and aesthetics were higher, and subjective sense of creative ownership was lower.
- Evidence / limitation
- Evidence: moderate (short-term subjective experience only). Small sample, different stories, mostly self-report; cannot show long-term learning or change in minors.
- Implication
- YuCROWN inference / candidate exploration Earth Library could let authors voluntarily attach "the feeling I originally meant to express / what I kept or changed," observing visual quality and expressive agency separately.
- Youth exploration
- Candidate exploration Does AI make my story more like me, or more like common images? Start: pick a non-sensitive everyday moment, write three sentences of your own description, compare one AI suggestion, and note one thing kept and one rejected; can be kept private, no public disclosure required.
- Source
- Full text (arXiv HTML) · metadata · conference program check · double-blind review rules
2. The impact of human-AI co-creation on perceived creativity in generative AI art: a serial mediation mechanism of psychological ownership, perceived agency, and creative flow Verified
- Authors
- Chenyang Wang, Miao Chen, Wen Yang
- Type / status
- Frontiers in Psychology, 2026-10-09; peer-reviewed original research.
- DOI
- 10.3389/fpsyg.2026.1986050
- Accessed
- 2026-10-18
- Design
- Two randomized online experiments, 600 participants in China, mean age about 24.
- Bounded finding
- Co-creation received higher subjective creativity ratings than AI-only generation; among the three workflows tested in the second experiment, the middle condition rated best.
- Evidence / limitation
- Evidence: moderate. Multiple operating permissions were bundled and some task outputs were not equivalent, so "half AI involvement is best" cannot be inferred; concurrently measured psychological mediators do not prove a causal chain; no conclusions about minors. It measures psychological ownership, not copyright.
- Implication
- YuCROWN inference / candidate exploration Study could first compare "which substantive choices the author can make," rather than grading by proportion of AI use; final-choice reasons could be recorded voluntarily.
- Youth exploration
- Candidate exploration Does the power to change a suggestion's content, or only to pick the final image, affect the feeling of "this is my work" more? Start: in one small piece, try "select only" and "may modify" separately and note your own judgment; this is personal exploration, not a replication of the randomized experiment.
- Source
- frontiersin.org — full text
3. How generative AI becomes embedded in design learning: a 14-instructional-day intensive longitudinal study of reciprocal dynamics between AI use and self-efficacy Verified
- Authors
- Yang Li, Yi Yang, Silu Bao
- Type / status
- Frontiers in Psychology, 2026-10-14; peer-reviewed original research.
- DOI
- 10.3389/fpsyg.2026.1975841
- Accessed
- 2026-10-18
- Design
- 260 Chinese undergraduate design students aged 18–23; diaries over 14 instructional days, 3,558 entries; Bayesian dynamic structural equation modeling.
- Bounded finding
- AI use and next-instructional-day self-efficacy showed small bidirectional predictive associations.
- Evidence / limitation
- Evidence: moderate (association), low (causal interpretation). Self-report, short-term, non-randomized; confidence is not actual ability or independent transfer; no minor sample.
- Implication
- YuCROWN inference / candidate exploration Study could record "more confident today" and "can I explain a choice myself tomorrow" separately, rather than treating usage frequency as evidence of dependence or growth.
- Youth exploration
- Candidate exploration When do I most want to use AI — when stuck, or when confident? Start: voluntarily write five one-minute private notes on the task, why you asked for help, and your own explanation; do not draw general causal conclusions from a personal diary.
- Source
- frontiersin.org — full text
4. Ways Generative AI Can Support and Threaten Learning in K-20 US Education Verified
- Authors
- Panayiota Kendeou, Jeffrey A. Greene, Nia Nixon et al.
- Type / status
- University of Minnesota CEHD website, October 2026 (no specific day given). Institution-published expert-consensus research report; not a verified peer-reviewed journal article, and no verified DOI.
- Accessed
- 2026-10-18
- Design
- Structured Delphi; 30 experts in the first round, 29 participating in the second and 28 providing quantitative ratings; 65 learning processes mapped.
- Bounded finding
- Experts judged that risks and supportive opportunities vary by learning stage, so final output alone is not enough to look at.
- Evidence / limitation
- Evidence: moderate (consensus map), low (causal proof of program effects). Limited by expert selection and the US education context; not a student intervention trial, and expert agreement is not an effect size.
- Implication
- YuCROWN inference / candidate exploration Study could test "observe and pose your own question first → use AI to clarify and compare → switch AI off and explain your understanding → hear one person's response" as a sequence to be tested.
- Youth exploration
- Candidate exploration At which step does AI help me understand, and at which does it skip my thinking? Start: on a topic like a small boat or an everyday observation, keep one original question, one verified source, and one independent explanation; do not claim this sequence is validated by the report.
- Source
- cehd.umn.edu — expert consensus report
These studies differ in method and population; effect sizes are not merged, and none is evidence of EarthLinC or YuCROWN outcomes. A minimal exploration for Cali to discuss: voluntarily keeping four fragments — "original question, one key trade-off, independent explanation, another person's response" — with the author choosing whether to share. Sense of authorship, credit, permission to share experience, and legal ownership in AI-assisted processes should be handled separately; this issue reaches no new conclusion on legal rights. Applicability to projects with minors still needs separate assessment.
De-duplication index
| Date | Normalized original title | DOI / ID | Version / status | Relation to existing entries | Checked |
|---|---|---|---|---|---|
| 2026-10-06 (open manuscript) | Drawing the Line: Where AI Guidance and Human Creativity Meet in Emotion-Driven Comic Storyboarding | arXiv:2610.09155v1 · 10.48550/arXiv.2610.09155 | ACII 2026 peer-reviewed conference study; arXiv v1 open manuscript | New; the EmoToon open manuscript and the ACII paper are the same study and must not be recommended as a new paper next issue | 2026-10-18 |
| 2026-10-09 | The impact of human-AI co-creation on perceived creativity in generative AI art: a serial mediation mechanism of psychological ownership, perceived agency, and creative flow | 10.3389/fpsyg.2026.1986050 | Frontiers in Psychology, peer-reviewed original research | New; no overlap with A–L or the 21 sources | 2026-10-18 |
| 2026-10-14 | How generative AI becomes embedded in design learning: a 14-instructional-day intensive longitudinal study of reciprocal dynamics between AI use and self-efficacy | 10.3389/fpsyg.2026.1975841 | Frontiers in Psychology, peer-reviewed original research | New; no overlap with A–L or the 21 sources | 2026-10-18 |
| 2026-10 (no specific day) | Ways Generative AI Can Support and Threaten Learning in K-20 US Education | No verified DOI; fixed CEHD URL | Institution-published expert-consensus report, not verified peer-reviewed | New; no overlap with A–L or the 21 sources | 2026-10-18 |
| 2026-10-21 | Prompt specificity and iterative behaviour in text-to-music co-creation: a secondary analysis of interaction logs and a framework for perceived creative control | 10.3389/fpsyg.2026.1965465 | Frontiers in Psychology, peer-reviewed Original Research | New; no historical duplicate | 2026-10-23 |
| 2026-10-21 | A diagnostic human agency framework for AI driven education from a PRISMA guided systematic review | 10.1007/s44217-026-02173-7 | Discover Education, peer-reviewed Review/conceptual framework | New; no historical duplicate | 2026-10-23 |
| 2026-10-19 (accepted) | Owning What One Did Not Make: Psychological Ownership, Agency, and the Authorship Legitimation Deficit in Human–AI Co-Creation | 10.3389/fpsyg.2026.1928186 | Frontiers in Psychology, peer-reviewed accepted Conceptual Analysis; abstract-only | Listed 2026-10-23 on the accepted abstract; formally published with full text 2026-10-28; full text verified 2026-10-30; same-study version update, not counted as a new recommendation | 2026-10-23 |
| 2026-10-20 (accepted) | Generative AI Configurations Across Creative Phases: A 2 × 2 Experiment on Creative Performance and Immediate Knowledge in Art and Design Education | 10.3389/fpsyg.2026.1971021 | Frontiers in Psychology, peer-reviewed accepted Original Research; abstract-only (2026-10-23); current status: formally published 2026-11-04, full text verified (2026-11-06) | New; when the formal full text appears, record it as a version update, not as a new study to recommend again; formally published 2026-11-04 (Frontiers in Psychology, peer-reviewed Original Research), full text verified 2026-11-06 (see Issue 4); original 2026-10-23 abstract-only checked date preserved | 2026-10-23 |
| 2026-10-24 (online) / 2026-10-26 (VoR) | Student epistemic agency in AI-mediated practices | 10.1186/s40561-026-00464-y | Smart Learning Environments, peer-reviewed Research article; full text verified | New; no historical duplicate | 2026-10-30 |
| 2026-10-28 | Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy | 10.3389/fpsyg.2026.1975833 | Frontiers in Psychology, peer-reviewed Original Research; full text verified | New; no historical duplicate | 2026-10-30 |
| 2026-10-27 | Development and validation of the AI cognitive offloading scale for Chinese college students | 10.3389/fpsyg.2026.1884676 | Frontiers in Psychology, peer-reviewed Original Research; full text verified | New; no historical duplicate | 2026-10-30 |
| 2026-10-26 | Catalysts, Not Only Constraints: Pre-Service EFL Teacher Agency in Generative AI-Assisted Lesson Planning | 10.3390/bs16111998 | Behavioral Sciences 16(11):1998, peer-reviewed original qualitative case study; full text verified | New; no historical duplicate | 2026-10-30 |
| 2026-11-03 | Who Is Doing the Thinking? Characterising Human Agency in First-Year Students’ AI Interactions | arXiv:2611.04783v1 · 10.48550/arXiv.2611.04783 | arXiv v1 preprint; peer review unverified; full text verified | New; no overlap with A–L, the 21 sources or the previous three issues | 2026-11-06 |
| 2026-11-04 | Measuring the collaboration, not the agent (expanded full-text title: Measuring the Collaboration, Not the Agent: An Evaluation Framework for Human–AI High-Dimensional Visual Analytics) | arXiv:2611.06487v1 · 10.48550/arXiv.2611.06487 | arXiv v1 preprint / framework and simulation; listed in the IEEE VIS 2026 VISxGenAI workshop program (9 Nov), peer review unverified; full text verified | New; both titles are the same study and must not later be recommended again as new research | 2026-11-06 |
| 2026-11-02 | CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances | arXiv:2611.03389v1 · 10.48550/arXiv.2611.03389 | arXiv v1 open manuscript; author site states IEEE IS2 2026 Accepted; review and final DOI unverified; full text verified | New; the arXiv v1 and the author conference manuscript are the same study and must not later be recommended again as new research | 2026-11-06 |
| 2026-11-09 | Generative AI use and scientific literacy among 15-year-olds in Beijing, Shanghai, Jiangsu, and Zhejiang, China: the mediating role of academic help-seeking | 10.3389/fpsyg.2026.2013021 | Frontiers in Psychology, peer-reviewed Original Research; full text verified | New; no overlap with A–L, the 21 sources or the previous four issues | 2026-11-13 |
| 2026-11-09 | Perceived Agency During Agent–Avatar Control Transitions in Virtual Reality | arXiv:2611.12529v1 · 10.48550/arXiv.2611.12529 | arXiv v1 preprint; peer review / formal venue unverified; full text verified | New; a later conference version of the same study must be merged as a version update, not recommended again as new research | 2026-11-13 |
| 2026-11-12 (accepted) | AI and the responsibility-bearing status of authorship | No verifiable DOI; institutional record | Philosophy & Technology, peer-reviewed accepted conceptual/philosophical analysis; abstract only, accepted manuscript embargoed | New; de-duplicated by normalized title + author + institutional URL; record full publication as a version update | 2026-11-13 |
- Section M only: Issue 5 (2026-11-13) with 3 new items (two full texts, one abstract only), 3 new de-duplication index rows, and an issue de-duplication list added; column status changed to five issues listed, next routine issue 2026-11-20; Issues 1–4 fully preserved.
- The base reference research date 2026-10-03 was not reset; the full text of Sections A–L and the dates and verification statuses of the 21 base sources are unchanged; no older entry's checked date was blanket-reset to verified.
- Sidebar "Research Watch updated" changed to 2026-11-13 (Issue 5).
- Section M only: Issue 4 (2026-11-06) with 3 new items, 1 version update, 3 new de-duplication index rows, and an issue de-duplication list added; column status changed to four issues listed, next routine issue 2026-11-13; Issues 1–3 fully preserved.
- The index record for 10.3389/fpsyg.2026.1971021 was extended and a formal-full-text note appended to its Issue 2 entry; the original 2026-10-23 checked date is unchanged and the original text is not deleted.
- The base reference research date 2026-10-03 was not reset; the full text of Sections A–L and the dates and verification statuses of the 21 base sources are unchanged; no older study was re-labelled as newly verified.
- Sidebar "Research Watch updated" changed to 2026-11-06 (Issue 4).
- Section M only: Issue 3 (2026-10-30) with 4 new studies, 1 version update, 4 new de-duplication index rows, and an issue de-duplication list added; column status changed to three issues listed, next routine issue 2026-11-06; Issues 1 and 2 fully preserved.
- The index record for 10.3389/fpsyg.2026.1928186 was extended and a full-text version note added to its Issue 2 entry; original listing and checked dates are unchanged.
- The base reference research date 2026-10-03 was not reset; the full text of Sections A–L and the dates and verification statuses of the 21 base sources are unchanged.
- Sidebar "Research Watch updated" changed to 2026-10-30 (Issue 3).
- Section M only: Issue 2 (2026-10-23) with 4 items, 4 new de-duplication index rows, and a DOI / title list added; column status changed to two issues listed, next routine issue 2026-10-30; Issue 1 content fully preserved.
- The base reference research date 2026-10-03 was not reset; the full text of Sections A–L and the dates and verification statuses of the 21 base sources are unchanged.
- Verification depth: full text verified for items 1 and 2, abstract only for items 3 and 4; no older entry was re-labelled as verified.
- Sidebar "Research Watch updated" changed to 2026-10-23 (Issue 2), displayed separately from the base research date.
- Section M only: first issue (2026-10-18) with 4 items and a de-duplication index added; column status changed to "first issue listed," next routine issue 2026-10-23.
- Full text read for all 4 items (4/4). The base reference research date 2026-10-03, the full text of Sections A–L, and the dates and verification statuses of the 21 sources are unchanged.
- Sidebar now shows "Research Watch updated: 2026-10-18," displayed separately from the base research date.
This issue updates the Research Watch column only. It does not update the base reference research date (2026-10-03) and does not change the dates or verification statuses of Sections A–L or the 21 base sources.