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Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale 衡量不同类型和领域的人工智能知识：开发和验证基于绩效的量表

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compedu.2026.105573

Inbal Klein-Avraham , Rut Ston , Osnat Atias , Ido Roll , Ayelet Baram-Tsabari

As artificial intelligence (AI) and generative AI (GenAI) technologies become increasingly integrated into everyday life, the need for validated tools that measure people's knowledge about AI grows. Here, we present the development and validation of a theoretically driven, performance-based scale for assessing AI and GenAI knowledge. The scale is grounded in a two-axial framework. One axis captures three knowledge types: content knowledge (what AI is and where it is encountered), procedural knowledge (how AI systems operate and are used), and epistemic knowledge (what features and construction processes characterize AI outputs). The other axis encompasses three knowledge domains: technology-related knowledge (AI systems), user-related knowledge (users' interaction with AI), and society-related knowledge (the social and ethical implications of AI). Based on an online survey of 800 internet-using adults from Israel, the 26-item scale was evaluated using confirmatory factor analysis, which demonstrated an acceptable model fit. It was further validated through two-stage structural equation modeling and group comparisons. Overall, the scale was found to be both valid and practically insightful: while it reproduces the expected relationships with additional constructs (e.g., trust in GenAI, attitudes toward AI) and expected differences between demographic groups, it also provides nuanced insights on the intricacies of AI knowledge. For example, the scale indicates that the relationship between trust in GenAI and knowledge about AI is grounded in both epistemic and societal knowledge. Thus, this novel tool affords more precise investigations into how different types and domains of AI knowledge relate to perceptions, behaviors, and decision-making in an AI-mediated world.

随着人工智能（AI）和生成式人工智能（GenAI）技术越来越多地融入日常生活，对衡量人们对人工智能知识的有效工具的需求也在增长。在这里，我们提出了一个理论驱动的、基于绩效的评估AI和GenAI知识的量表的开发和验证。天平在一个双轴框架中接地。一个轴捕获三种知识类型：内容知识（人工智能是什么以及在哪里遇到它），程序知识（人工智能系统如何运行和使用）和认知知识（人工智能输出的特征和构建过程）。另一个轴包含三个知识领域：与技术相关的知识（人工智能系统），与用户相关的知识（用户与人工智能的交互）和与社会相关的知识（人工智能的社会和伦理影响）。基于对来自[国家]的800名上网成年人的在线调查，采用验证性因子分析对26项量表进行评估，结果表明模型拟合可接受。通过两阶段结构方程建模和分组比较进一步验证。总体而言，该量表被发现既有效又具有实际洞察力：虽然它再现了与其他结构（例如，对GenAI的信任，对AI的态度）的预期关系以及人口群体之间的预期差异，但它也提供了对AI知识复杂性的细致入微的见解。例如，该量表表明，对GenAI的信任与对AI的了解之间的关系建立在认知知识和社会知识的基础上。因此，这个新工具可以更精确地研究人工智能知识的不同类型和领域如何与人工智能介导的世界中的感知、行为和决策相关。

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引用次数: 0

Exploring job burnout among family child care providers who participate in the child care subsidy system 参与托儿补助制度的家庭托儿人员职业倦怠的研究

IF 3.1 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Early Childhood Research Quarterly

Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.ecresq.2026.01.009

Yoonsook Ha , Juliann Nicholson , Paripoorna Baxi , Roberto S. Salva , Pamela Joshi , Kate Giapponi Schneider

There is little research on the well-being of family child care (FCC) providers who participate in the child care subsidy system, despite concerns about their declining numbers in the child care market. Drawing on survey data from a sample of 903 subsidized FCC providers in Massachusetts, this study examined an array of potential correlates associated with provider burnout. Guided by the Job Demands-Resources model, the study employed sequential linear regression models using the block method to assess the associations between job demands and job resources and burnout, controlling for provider demographic characteristics. Multivariate regression results showed that job demand variables representing administrative challenges, children’s social-emotional challenges, and a lack of parent engagement were significantly associated with increases in burnout. Related to job resources, providers’ utilization of supportive services for children and families and access to professional development were significantly associated with decreases in burnout. Findings suggest that enhancing access to professional development and community resources to support underserved children and reducing administrative burden associated with the subsidy system may be potential strategies for mitigating burnout among subsidized FCC providers.

参与儿童保育补贴制度的家庭儿童保育（FCC）提供者的福利研究很少，尽管人们担心他们在儿童保育市场中的数量下降。根据对马萨诸塞州903家联邦通信委员会（FCC）补贴提供商样本的调查数据，本研究调查了一系列与提供商倦怠相关的潜在相关性。本研究以工作需求-资源模型为指导，在控制医务人员人口学特征的前提下，采用分块法的序贯线性回归模型评估工作需求、工作资源和职业倦怠之间的关系。多元回归结果显示，代表行政挑战、儿童社会情感挑战和缺乏父母参与的工作需求变量与倦怠的增加显著相关。在工作资源方面，提供者对儿童和家庭支持服务的利用以及获得专业发展的机会与职业倦怠的减少显著相关。研究结果表明，增加获得专业发展和社区资源的机会，以支持服务不足的儿童，减少与补贴制度相关的行政负担，可能是减轻补贴FCC提供者倦怠的潜在策略。

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引用次数: 0

Expressions of learner agency in virtual inquiry: Linking agency and evidence-centered game design 虚拟探究中学习者代理的表现：关联代理与循证游戏设计

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.compedu.2026.105590

Jillianne Code , Kieran Forde , Rachel Moylan , Aimee Lutrin , Zahira Tasabehji , Rachel Ralph , Aashay Mehta , Nick Zap , Nesrine El Banna

Game-based learning environments often support exploration but rarely connect learner agency with rigorous, embedded assessment. This study reports on the design and pilot implementation of ALIVE (Agency for Learning in Immersive Virtual Environments), a virtual inquiry environment that integrates the Agency for Learning framework with Evidence-Centered Game Design. Nine middle and high school students completed an ecological investigation that required evidence collection, hypothesis testing, and causal explanation. Data included think-aloud protocols, gameplay logs, and brief feedback questions. Triangulated analyses captured both convergence and divergence between self-reported and observed agency. Learners showed intentional decisions, strategy shifts, and selective delegation to system supports at points of uncertainty. These findings show how aligned competency, evidence, and task models make inquiry actions visible and interpretable. The study also offers a multisource approach for examining expressions of agency within guided digital inquiry. Limitations include the small sample and single-session design. Future work should examine longer-term patterns, broader implementation, and transfer across domains.

基于游戏的学习环境通常支持探索，但很少将学习者代理与严格的嵌入式评估联系起来。本研究报告了ALIVE（沉浸式虚拟环境中的学习代理）的设计和试点实施，这是一个虚拟探究环境，将学习代理框架与以证据为中心的游戏设计相结合。九名初高中学生完成了一项生态调查，包括证据收集、假设检验和因果解释。数据包括大声思考协议、玩法日志和简短的反馈问题。三角分析捕捉到了自我报告和观察到的代理之间的趋同和分歧。学习者表现出有意的决策，策略转变，并在不确定的情况下选择性地委托给系统支持。这些发现显示了一致的能力、证据和任务模型如何使调查行动可见和可解释。该研究还提供了一种多源方法来检查引导数字查询中的代理表达。限制包括小样本和单会话设计。未来的工作应该检查更长期的模式、更广泛的实现和跨领域的转移。

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引用次数: 0

Kindergarten Health and Absenteeism: Implications for Academic Achievement and Racial and Ethnic Disparities 幼儿园健康与缺勤：对学业成就与种族与民族差异的影响

IF 3.1 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Early Childhood Research Quarterly

Pub Date : 2026-07-01 Epub Date: 2026-02-07 DOI: 10.1016/j.ecresq.2026.01.011

Lily Steyer , Javier Omar , Michael J. Sulik, Jelena Obradović

Leveraging data from the San Francisco Unified School District, this study shows that everyday early childhood health concerns reported by teachers significantly predict children’s spring kindergarten academic outcomes, even after accounting for widely considered covariates (N = 8,146; female = 48%; M_age = 5.47; Asian American = 27%, Black = 5%, Latine = 21%, Multiracial/Other = 23%, White = 14%). Teacher-reported hunger, tiredness, and sickness were primarily negatively associated with academic outcomes directly, rather than indirectly via absenteeism, consistent with everyday health concerns affecting engagement and performance at school. However, absenteeism accounted for a modest proportion (12-21%) of these associations, suggesting it is one pathway through which health impacts learning. Our findings suggest that for Black kindergarteners, everyday health concerns contribute to inequities in spring academic outcomes relative to their White, Asian American, and Latine peers. Results underscore the utility and practicality of universal early childhood health screening by teachers and highlight everyday health concerns as a promising lever for advancing racial and ethnic equity in early elementary skills.

利用旧金山联合学区的数据，这项研究表明，即使在考虑了广泛考虑的共变量（N = 8,146；女性= 48%;Mage = 5.47；亚裔美国人= 27%，黑人= 5%，拉丁裔= 21%，多种族/其他= 23%，白人= 14%）之后，教师报告的幼儿日常健康问题也能显著预测儿童春季幼儿园的学业成绩。教师报告的饥饿、疲劳和疾病主要与学习成绩直接负相关，而不是通过缺勤间接相关，这与影响学校参与度和表现的日常健康问题一致。然而，缺勤占这些关联的适度比例（12-21%），表明这是健康影响学习的一个途径。我们的研究结果表明，与白人、亚裔美国人和拉丁裔同龄人相比，黑人幼儿园儿童的日常健康问题导致了他们春季学业成绩的不平等。结果强调了教师普遍幼儿健康筛查的效用和实用性，并强调了日常健康问题作为促进早期基本技能种族和民族平等的有希望的杠杆。

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引用次数: 0

From automation to thinking: The role of AGI in discourse analysis of computer-supported collaborative learning based on computational grounded theory 从自动化到思考：基于计算基础理论的AGI在计算机支持的协同学习话语分析中的作用

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-24 DOI: 10.1016/j.compedu.2026.105579

Tien-Chih Chang , Alice R.P. Li , Chia-Yu Wang , John J.H. Lin

Analyzing the complex dialogue central to computer-supported collaborative learning is crucial for understanding learning processes, yet remains a significant challenge for educational researchers due to the labor-intensive nature of manual coding and the semantic limitations of traditional computational methods. Recent advancements have highlighted the potential of Large Language Models (LLMs) to move beyond mere automation, demonstrating an ability for inference without task-specific data that is characteristic of artificial general intelligence. To harness this potential, this study introduced and evaluated a human-AI collaborative framework (CGT-LLM) that integrates LLMs into computational grounded theory. Specifically, CGT-LLM focuses on learning analytics for rich discursive data. Applied to dialogue from a climate change collaborative simulation game, the framework was evaluated against a supervised bidirectional encoder representations from transformers (BERT) baseline. The performance of the framework approached human expert-level performance in categories related to explicit instructions, numerical data, or direct statements of intent crucial to game objectives, while also demonstrating promising capability in identifying more abstract and less obvious themes. The findings demonstrate that the researcher's role in computational grounded theory remains critical, particularly in exploring data diversity during the discovery phase, and making final interpretive judgments for abstract themes during the classification phase. This framework thus positions LLMs as a valuable assistant rather than as a replacement for human expertise, providing educators and researchers with a tool to gain deeper, more scalable insights into collaborative learning processes, and offering potential to inform the design of timely pedagogical interventions.

分析计算机支持的协作学习的复杂对话对于理解学习过程至关重要，但由于手工编码的劳动密集型性质和传统计算方法的语义限制，对教育研究人员来说仍然是一个重大挑战。最近的进展突出了大型语言模型（llm）超越单纯自动化的潜力，展示了不需要特定任务数据的推理能力，这是人工智能的特征。为了利用这一潜力，本研究引入并评估了一个人类-人工智能协作框架（CGT-LLM），该框架将llm整合到计算基础理论中。具体来说，CGT-LLM侧重于学习分析丰富的话语数据。将该框架应用于气候变化协作模拟游戏中的对话，并根据来自变压器（BERT）基线的监督双向编码器表示对其进行评估。在与明确指令、数字数据或对游戏目标至关重要的直接意图陈述相关的类别中，该框架的表现接近人类专家水平，同时在识别更抽象和不太明显的主题方面也显示出有希望的能力。研究结果表明，研究人员在计算基础理论中的作用仍然至关重要，特别是在发现阶段探索数据多样性，以及在分类阶段对抽象主题做出最终解释性判断。因此，该框架将法学硕士定位为有价值的助手，而不是人类专业知识的替代品，为教育工作者和研究人员提供了一种工具，以获得对协作学习过程更深入、更可扩展的见解，并为及时的教学干预设计提供了信息。

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引用次数: 0

When time is relative: Pre-K classroom time use is measure sensitive 当时间是相对的：学前班的课堂时间使用是测量敏感的

IF 3.1 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Early Childhood Research Quarterly

Pub Date : 2026-07-01 Epub Date: 2026-02-11 DOI: 10.1016/j.ecresq.2026.01.008

Paola Guerrero Rosada , Christina Weiland , Meghan McCormick , Catherine Snow , JoAnn Hsueh , Michelle Maier , Jason Sachs

Time use measures are used to characterize children’s instructional experiences in pre-kindergarten (Pre-K). Although time use is an actionable aspect of Pre-K quality that can be measured with minimal levels of inference, evidence on how it relates to children’s academic gains is not conclusive. We compared two time use measures, Narrative Record (Farran et al., 2015) and Individualizing Student Instruction (Connor et al., 2009), to explore whether time use and its associations with children’s gains are consistently captured across instruments. These two measures differ in time-sampling strategies (i.e., second-to-second and episode-based) and level of data collection (i.e., child vs. classroom level). Using the same set of classroom videos, we directly compared descriptive statistics and profiles across the measures while holding child and classroom characteristics constant. Our sample included 35 Boston Public Schools prekindergarten classrooms and 247 children. Findings revealed that time spent in learning activities and learning settings, as well as time-use profiles, varied depending on the measure. Although we replicated profiles observed in prior research, classrooms were not consistently assigned to profiles due to variable-level measurement error. Moreover, instructional profiles did not consistently predict children’s academic or cognitive gains: language and working memory were unrelated to time-use profiles, while math and executive function associations were sensitive to modeling decisions. Results highlight the need for further measurement research to accurately record classroom time use and to identify practices that most effectively support young children’s learning gains.

时间使用测量是用来描述儿童在学前班（Pre-K）的教学经验。虽然时间利用是学前教育质量的一个可操作的方面，可以用最小程度的推断来衡量，但关于它与儿童学业成绩的关系的证据并不是决定性的。我们比较了两种时间使用测量方法，叙述性记录（Farran等人，2015）和个性化学生指导（Connor等人，2009），以探索时间使用及其与儿童收益的关联是否在各种工具中一致地被捕获。这两种测量方法在时间采样策略（即，秒对秒和基于情节）和数据收集水平（即，儿童与课堂水平）方面有所不同。使用同一组课堂视频，我们在保持儿童和课堂特征不变的情况下，直接比较了不同措施的描述性统计数据和概况。我们的样本包括35所波士顿公立学校的学前班教室和247名儿童。研究结果显示，在学习活动和学习环境中花费的时间，以及时间使用概况，因测量而异。虽然我们复制了先前研究中观察到的概况，但由于可变水平测量误差，教室并没有一致地分配到概况。此外，教学概况并不能始终如一地预测儿童的学业或认知收益：语言和工作记忆与时间使用概况无关，而数学和执行功能关联对建模决策敏感。结果强调需要进一步的测量研究，以准确记录课堂时间的使用，并确定最有效地支持幼儿学习收益的做法。

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引用次数: 0

Generative artificial intelligence augments social interactivity and learning outcomes: Advancing the framework of a scaffolded human–GenAI shared agency 生成式人工智能增强社会互动性和学习成果：推进搭建的人类-基因共享代理框架

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-08 DOI: 10.1016/j.compedu.2026.105564

Yi-Chen Juan , Yuan-Hsuan Lee , Jiun-Yu Wu

Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human–GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twenty-eight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included k-modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: Human-GenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI’s capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, offering pedagogical implications to foster active student participation and cultivate learner agency within the symbiotic Human–GenAI partnership.

生成式人工智能（GenAI）不仅是人类知识建构的工具，而且是人类知识建构的积极合作者；然而，人类与基因的互动动力学仍未得到充分探索。本研究调查了统计学习社区中人类与基因的交互概况、网络交互性和概况差异，以及将人类与基因的交互与学习绩效联系起来的潜在机制。在为期16周的高级统计学课程中，我们设计了人类-基因ai调查和问题解决支架，以促进28名研究生和基因ai在7项家庭作业中的共享代理。分析方法包括k模式聚类、社会网络分析和偏最小二乘结构方程模型，并辅以互动概况的案例研究。确定了三种不同的人类-GenAI交互概况：人类-GenAI合作者，GenAI协助下的同伴合作者，以及晚期采用GenAI的个人学习者。随着GenAI在学习社区中占据中心枢纽角色，网络交互性变得紧密。然后，这些模型通过度中心性（直接连接的数量）和同伴提名作为助手，展示了人类-基因- ai交互影响学习表现的独特途径。这些案例研究强调了GenAI增强人类角色的能力，鼓励更深入的探究，扩大同行讨论的深度，或促进对各种问题解决策略的探索。这些发现为搭建人类-基因共享代理的框架提供了经验证据，为促进学生的积极参与和利用基因ai的潜力培养学习者代理和人类-基因共生知识构建提供了教学启示，从而增加了理论和实践的价值。

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引用次数: 0

Virtual reality serious games for promoting environmental systems thinking and pro-environmental policy support 虚拟现实严肃游戏为促进环境系统思考和亲环境政策提供支持

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-14 DOI: 10.1016/j.compedu.2026.105575

Joseph G. Guerriero , Pejman Sajjadi , Janet K. Swim , Alexander Klippel , Jamie DeCoster , Mahda M. Bagher

Virtual reality (VR) serious games can expose people to environmental processes they would not otherwise experience. This can make topics in environmental science more concrete to learners, improving learning outcomes and downstream behaviors related to environmental sustainability. In a randomized experiment (N = 189), we examined the effectiveness of a VR serious game designed to teach people about a topic in environmental science—the Critical Zone—by comparing it to a non-VR version of the game and to a static presentation of the same information on a website. Although the VR serious game promoted greater spatial presence and feelings of awe (which, in turn, translated to feeling more connected with nature), these effects did not translate to improved learning outcomes and pro-environmental policy support as we hypothesized across two separate models. Yet, exploratory analyses revealed a very small but significant indirect pathway by which the VR serious game promoted systems thinking about the Food-Energy-Water (FEW) nexus and pro-environmental policy support: VR (vs other learning formats) led to increases in a sense of spatial presence, then to perceived learning effectiveness, then to FEW systems thinking, and, finally, to pro-environmental policy support. Our results shed light on the mixed effect of VR and spatial presence on learning outcomes discussed in the wider literature on VR in education. Although the original hypotheses were largely unsupported, by exploring and highlighting pathways from learning formats to outcomes, we demonstrate the potential of VR for promoting learning and pro-environmental policy support.

虚拟现实（VR）严肃游戏可以让人们接触到他们原本不会经历的环境过程。这可以使环境科学的主题对学习者来说更加具体，从而改善与环境可持续性相关的学习成果和下游行为。在一项随机实验（N = 189）中，我们通过将VR严肃游戏与非VR版本的游戏以及网站上相同信息的静态呈现进行比较，检验了VR严肃游戏的有效性，该游戏旨在教授人们有关环境科学的主题——关键区域。尽管VR严肃游戏提升了更大的空间存在感和敬畏感（这反过来又转化为与自然联系更紧密的感觉），但正如我们在两个独立模型中假设的那样，这些效果并没有转化为改善的学习成果和亲环境政策支持。然而，探索性分析揭示了一个非常小但重要的间接途径，即VR严肃游戏促进了对食物-能源-水（FEW）关系的系统思考和亲环境政策支持：VR（相对于其他学习形式）导致空间存在感的增加，然后是感知学习效率的增加，然后是FEW系统思考，最后是亲环境政策支持。我们的研究结果揭示了VR和空间存在对学习结果的混合影响，这些影响在更广泛的关于VR教育的文献中讨论过。虽然最初的假设在很大程度上没有得到支持，但通过探索和强调从学习形式到结果的途径，我们证明了虚拟现实在促进学习和亲环境政策支持方面的潜力。

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引用次数: 0

Participation across public programs among children in low-income families: Public preschool, child care subsidies, and the supplemental nutrition assistance program 低收入家庭儿童参与公共项目：公立学前教育、儿童保育补贴和补充营养援助项目

IF 3.1 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Early Childhood Research Quarterly

Pub Date : 2026-07-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ecresq.2026.01.007

Michah W. Rothbart , Colleen Heflin , Taryn W. Morrissey , Xiaohan Sun

Early care and education (ECE) and nutrition assistance programs expand opportunity during early childhood. Although prior research examines participation in individual ECE programs, few investigate how families combine support across programs despite overlap in eligibility, or how nutrition assistance influences these decisions. Using linked administrative data from Virginia (2015–2019), this study examines how low-income families bundle public preschool with child care subsidies, and the extent to which the Supplemental Nutrition Assistance Program (SNAP) changes these bundles during the pre-K year. We explore who combines which ECE programs, when combinations occur, and how these patterns relate to hours in ECE using a sample of 176,773 first-time kindergarteners eligible for free or reduced-price lunch. We then focus on 98,965 of these children who participated in SNAP during the pre-K year. Child care subsidy participation is greatest among low-income children in private ECE, but also common among public pre-K participants, suggesting mismatches between preschool and parent work schedules. Subsidy participation is associated with greater weekly hours in licensed ECE across public, private, and other settings. Black children from low-income households are most likely to attend public ECE and participate in subsidies, whereas Hispanic children are least likely to participate in public ECE, subsidies, and SNAP. We show subsidy timing varies by ECE type: utilization peaks during the academic year for those in private ECE and declines in summer, but ticks up in summer for those in public ECE or other settings. Findings highlight the importance of public program coordination in supporting low-income families.

早期护理和教育（ECE）以及营养援助项目扩大了儿童早期的机会。尽管先前的研究考察了各个ECE项目的参与情况，但很少有人调查家庭如何在资格重叠的情况下将不同项目的支持结合起来，或者营养援助如何影响这些决定。本研究使用弗吉尼亚州（2015-2019）的相关行政数据，研究了低收入家庭如何将公立幼儿园与儿童保育补贴捆绑在一起，以及补充营养援助计划（SNAP）在学前教育期间改变这些捆绑的程度。我们以176,773名有资格享受免费或减价午餐的第一次上幼儿园的儿童为样本，探讨了谁结合了哪些ECE课程，何时发生组合，以及这些模式与ECE学习时间的关系。然后，我们将重点放在98,965名在学前班期间参加SNAP的儿童身上。在私立ECE的低收入儿童中，儿童保育补贴的参与程度最高，但在公立pre-K参与者中也很常见，这表明学龄前儿童和父母的工作时间表不匹配。参与补贴与在公共、私人和其他环境中获得许可的ECE每周工作时间增加有关。来自低收入家庭的黑人儿童参加公共ECE和参加补贴的可能性最大，而西班牙裔儿童参加公共ECE、补贴和SNAP的可能性最小。我们显示，补贴时间因欧洲经委会类型而异：私立欧洲经委会的利用率在学年期间达到峰值，夏季下降，但公立欧洲经委会或其他机构的利用率在夏季上升。研究结果强调了公共项目协调在支持低收入家庭方面的重要性。

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引用次数: 0

The effects of critical thinking intervention on reliance behaviors, problem-solving quality, and creativity during human-Generative AI collaborative learning 人类生成人工智能协作学习中批判性思维干预对依赖行为、问题解决质量和创造力的影响

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-07-01 Epub Date: 2026-01-18 DOI: 10.1016/j.compedu.2026.105576

Chenyu Hou , Gaoxia Zhu , Yanzhi Liu , Vidya Sudarshan , Josephine Leng Leng Chong , Fannie Yifan Zhang , Michael Yong Heng Tan , Yew Soon Ong

As Generative AI becomes increasingly used in various educational contexts, understanding how students engage with these tools during collaborative problem-solving is critical. While prior research suggests that critical thinking is essential in human-AI problem-solving, few studies have examined how instructional interventions, targeting critical thinking, might shape their reliance behaviors and collaborative outcomes. This study investigates the effects of a critical thinking intervention embedded in a problem-based learning (PBL) environment where students are engaged with Generative AI. The intervention combined strategies that foster critical thinking, including authentic instruction, structured dialogue, and AI-supported peer mentoring, aiming to promote students' thoughtful engagement and improve problem-solving performance. Participants (N = 226) were assigned to experimental (with critical thinking interventions) or comparison (without critical thinking interventions) conditions. We used pre- and post-surveys to measure participants' trust, critical thinking, and AI reliance behaviors, and group reports and chat histories to assess their problem-solving quality and creativity. Results revealed that the intervention did not produce significant improvement in self-reported critical thinking, possibly due to the short intervention duration. However, the intervention led to a marginal reduction in students' thoughtless use of Generative AI and significantly reduced the direct adoption of AI-generated content. Notably, students in the intervention condition produced more creative solutions, demonstrating higher levels of originality and idea density in their group reports. These findings suggest that how students use Generative AI is critical, especially when it is almost impossible to control whether they use it or not. The study highlights the importance of designing interventions that cultivate students’ critical thinking to support creative human-AI problem-solving.

随着生成式人工智能越来越多地应用于各种教育环境，了解学生在协作解决问题过程中如何使用这些工具至关重要。虽然之前的研究表明，批判性思维在人类-人工智能问题解决中至关重要，但很少有研究调查针对批判性思维的教学干预如何影响他们的依赖行为和合作结果。本研究探讨了在学生参与生成式人工智能的基于问题的学习（PBL）环境中嵌入批判性思维干预的效果。干预措施结合了培养批判性思维的策略，包括真实的教学、结构化的对话和人工智能支持的同伴指导，旨在促进学生的深思熟虑参与，提高解决问题的能力。参与者（N = 226）被分配到实验（有批判性思维干预）或比较（没有批判性思维干预）条件。我们使用前后调查来衡量参与者的信任、批判性思维和人工智能依赖行为，并使用小组报告和聊天记录来评估他们解决问题的质量和创造力。结果显示，干预并没有显著改善自我报告的批判性思维，可能是由于干预时间短。然而，干预导致学生对生成式人工智能的轻率使用略有减少，并显著减少了对人工智能生成内容的直接采用。值得注意的是，在干预条件下的学生提出了更多创造性的解决方案，在他们的小组报告中表现出更高的原创性和想法密度。这些发现表明，学生如何使用生成式人工智能是至关重要的，尤其是在几乎不可能控制他们是否使用它的情况下。该研究强调了设计干预措施的重要性，这些干预措施可以培养学生的批判性思维，以支持创造性的人类-人工智能问题解决。

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