Computers and Education Artificial Intelligence最新文献_第6页

Predicting student performance: A comprehensive review of machine learning, deep learning, and explainable AI approaches 预测学生表现：全面回顾机器学习、深度学习和可解释的人工智能方法

Computers and Education Artificial Intelligence Pub Date : 2026-06-01 Epub Date: 2026-01-23 DOI: 10.1016/j.caeai.2026.100548

Salma Boujmiraz , Hassane Darhmaoui , Ahmed Drissi el maliani

{"title":"Predicting student performance: A comprehensive review of machine learning, deep learning, and explainable AI approaches","authors":"Salma Boujmiraz , Hassane Darhmaoui , Ahmed Drissi el maliani","doi":"10.1016/j.caeai.2026.100548","DOIUrl":"10.1016/j.caeai.2026.100548","url":null,"abstract":"<div><div>The application of Machine Learning (ML) and Deep Learning (DL) in Educational Data Mining (EDM) is revolutionizing the educational field. Researchers have been particularly interested in predicting student performance at an early stage. These early predictions can significantly benefit students’ learning experiences, allowing educators and other stakeholders to plan timely interventions. This review examines studies employing these technologies, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure the approach is transparent and replicable. This systematic review begins with preselecting a total of 281 records that go through a rigorous screening process. Then, 72 retained studies are analyzed to uncover the types of databases utilized, examine the most common features and labels in predictive models, and identify the prevalent ML and DL methods and the reasons for their selection. In addition, the review highlights the role of explainability in making complex models more interpretable and pedagogically meaningful. However, only a limited number of studies explicitly connect these predictive approaches to educational innovation. This review therefore emphasizes how predictive and explainable AI (XAI) can bridge that gap by supporting evidence-based teaching, adaptive learning, and more equitable decision-making in education.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100548"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Value-sensitive design in action: Designing student-centered intelligent tutoring systems with community college students and instructors 价值敏感设计在行动：设计以学生为中心的智能辅导系统与社区大学的学生和教师

Computers and Education Artificial Intelligence Pub Date : 2026-06-01 Epub Date: 2026-02-19 DOI: 10.1016/j.caeai.2026.100560

Eunhye Grace Ko (고은혜) , Joan E. Hughes

{"title":"Value-sensitive design in action: Designing student-centered intelligent tutoring systems with community college students and instructors","authors":"Eunhye Grace Ko (고은혜) , Joan E. Hughes","doi":"10.1016/j.caeai.2026.100560","DOIUrl":"10.1016/j.caeai.2026.100560","url":null,"abstract":"<div><div>Amidst increased interest in AI ethics in education, we delve into the design of human-centered AI intelligent tutoring systems (ITS) through Value Sensitive Design (VSD). We conducted design activities, engaging in discussions with community college students, instructors, and field experts including developers, and data scientists. The research culminated 16 designs incorporating human-in-the-loop, explainable AI, and privacy controls, emphasizing student-centricity in ITS. The study highlights the historical oversight of student and instructor values in learning platform design, demonstrating how VSD can integrate student-centered perspectives into ITS to promote trust, privacy, and agency, particularly for community college students in developmental courses. For practical insights, the study demonstrates human-centered AI principal application in ITS can fostering more ethical, effective, and student-centric educational technologies. Drawing from authentic learner and instructor experiences, this research offers perspectives on ethically and student-centered ITS design via the VSD process, expanding VSD's application into education.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Affective computing in online higher education: A systematic literature review 网络高等教育中的情感计算：系统文献综述

Computers and Education Artificial Intelligence Pub Date : 2026-06-01 Epub Date: 2025-11-20 DOI: 10.1016/j.caeai.2025.100499

Krist Shingjergji , Deniz Iren , Corrie Urlings , Roland Klemke

{"title":"Affective computing in online higher education: A systematic literature review","authors":"Krist Shingjergji , Deniz Iren , Corrie Urlings , Roland Klemke","doi":"10.1016/j.caeai.2025.100499","DOIUrl":"10.1016/j.caeai.2025.100499","url":null,"abstract":"<div><div>Although affective states play a crucial role in education, they are often difficult to communicate and observe in online learning environments. This challenge has led to growing research on systems that can automatically detect affective states. This systematic literature review used PRISMA to analyze 96 studies on affective computing in online higher education, published between 2019 and 2024. The findings show that the most frequently studied affective states include learning-centered states, such as engagement, confusion, frustration, sentiment, as well as basic emotions, such as happiness, anger, sadness, surprise, and fear.</div><div>Terminology often overlaps, and basic emotions are commonly used as proxies for learning-centered states. The most used modality is facial expression, with the dominant detection approach being deep learning, particularly convolutional neural networks. Most studies rely on self-collected datasets that, due to privacy concerns, are not publicly shared, limiting reproducibility and generalizability. FER2013, collected in a generic context, and DAiSEE, collected in an online educational setting, are the most used public datasets. A key limitation is that most systems are not evaluated in real classrooms, revealing a gap between technological development, and educational application. Ethical considerations are often overlooked, with privacy, when addressed, being the main focus. Finally, the review’s findings highlight the need for stronger integration between education and technology through interdisciplinary collaboration and real-world validation.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large language models for education: An open-source paradigm for automated Q&A in the graduate classroom 用于教育的大型语言模型：研究生课堂中自动化问答的开源范例

Computers and Education Artificial Intelligence Pub Date : 2026-06-01 Epub Date: 2026-01-14 DOI: 10.1016/j.caeai.2026.100546

Ryann M. Perez, Marie Shimogawa, Yanan Chang, Xinning Li, Hoang Anh T. Phan, Jason G. Marmorstein, Evan S.K. Yanagawa, E. James Petersson

{"title":"Large language models for education: An open-source paradigm for automated Q&A in the graduate classroom","authors":"Ryann M. Perez, Marie Shimogawa, Yanan Chang, Xinning Li, Hoang Anh T. Phan, Jason G. Marmorstein, Evan S.K. Yanagawa, E. James Petersson","doi":"10.1016/j.caeai.2026.100546","DOIUrl":"10.1016/j.caeai.2026.100546","url":null,"abstract":"<div><div>Large Language Models (LLMs) offer scalable educational support, but face barriers regarding accuracy, cost, and learning depth. To interrogate these limitations, we developed the Teaching Assistant for Specialized Knowledge (TAsk), a retrieval-augmented generation enabled and educator curated pipeline. In this nine-week pilot study (N = 33 participants), we deployed TAsk in a graduate-level biological chemistry course. We compared TAsk against human expert teaching assistants (TAs) using blinded review process and analyzed inquiry depth. We observed three major findings related to potential pedagogical decisions and educational theory. First, TAsk delivered effective feedback that was specific and adaptive as it significantly outperformed expert TAs in overall correctness. However, human TAs remained superior in tailoring responses to course nuances. Second, behavioral analysis based on educational scaffolding techniques, such as Bloom's Taxonomy and the Zone of Proximal Development (ZPD), identified a cognitive bypass risk where frequent users submitted significantly fewer higher-order queries compared to infrequent users. Third, benchmarking demonstrated that smaller models could approach frontier model performance when optimized, suggesting future costs can be reduced significantly for TAsk in the pilot study. Finally, we validated a confabulation detection algorithm, hypothesizing that this algorithm could help students calibrate trust in model outputs in future iterations of TAsk. Taken together, these contributions establish TAsk as a validated framework for higher education learning while highlighting the critical need for pedagogical scaffolding for LLMs.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100546"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and implementation of an intelligent assisted teaching system for Chinese English based on natural language processing and reinforcement learning 基于自然语言处理和强化学习的汉语英语智能辅助教学系统的开发与实现

Computers and Education Artificial Intelligence Pub Date : 2025-12-01 Epub Date: 2025-08-28 DOI: 10.1016/j.caeai.2025.100466

Xin Li

{"title":"Development and implementation of an intelligent assisted teaching system for Chinese English based on natural language processing and reinforcement learning","authors":"Xin Li","doi":"10.1016/j.caeai.2025.100466","DOIUrl":"10.1016/j.caeai.2025.100466","url":null,"abstract":"<div><div>In the proposed allocation scheme, an adaptive mechanism called learning decision maker is introduced to enable the DRL agent to interact with the communication system, determine the speed of network training optimization based on the time-varying information of the communication environment, and enable the network to train more efficiently. In addition, selecting memory blocks in previous work. The simulation results show that the addition of the learning decision maker mechanism can accelerate the convergence of the neural network and achieve a higher overall system rate. Customized systems are currently mainly aimed at fields such as composition and grammar, with few translation teaching platforms, and none of them include systems for the number of passive reflective elements in IRS is set to 16, a constant interval around 80 rounds. The Critic network is updated twice are set to 0.001 and 0.002, respectively. The optimizer for D3QN and TD3 networks is the Adam optimizer, which can correct weights, biases, and learning rates during operation. This article aims to implement an English translation teaching assistance system that supports intelligent analysis.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100466"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Do AI chatbot-integrated writing tasks influence writing self-efficacy and critical thinking ability? An exploratory study 人工智能聊天机器人集成的写作任务是否影响写作自我效能感和批判性思维能力？探索性研究

Computers and Education Artificial Intelligence Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.caeai.2025.100472

Jie Gao , Jing Zhang , Yalin Li

{"title":"Do AI chatbot-integrated writing tasks influence writing self-efficacy and critical thinking ability? An exploratory study","authors":"Jie Gao , Jing Zhang , Yalin Li","doi":"10.1016/j.caeai.2025.100472","DOIUrl":"10.1016/j.caeai.2025.100472","url":null,"abstract":"<div><div>Emerging discussions have boomed in relation to recent trends and applications of generative artificial intelligence in education, but few attention were paid to AI chatbots in terms of critical thinking ability and second language (L2) writing self-efficacy. To address such research gap, this study aims to delve into effectiveness of AI chatbot-integrated writing tasks on critical thinking ability and writing self-efficacy among vocational college students. Followed the quasi-experiment design, this study recruited 80 first-year vocational college students from two intact class. Throughout seven-week's instruction, quantitative data were collected with pre- and post-questionnaires of writing self-efficacy and critical thinking ability. The results revealed no statistical difference in terms of both critical thinking ability and writing self-efficacy even with greater increase among the experimental group than that of the control peers. Further analysis of each dimension demonstrated the statistical difference of language construction, indicating that vocational college students with limited language proficiency strive to promote language organization in their writings in terms of critical thinking ability development. The research contributes valuable empirical evidence of incorporating generative AI chatbots into L2 writing by extending the Cognitive Load Theory and Social Cognitive Theory. It also underscores the need to leveraging AI chatbots as tools for cognitive transformation rather than mere procedural assistance, and relevant training programs are essential to optimize the educational benefits of AI tools and cultivate digital literacy among vocational college learners.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100472"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the sustainability perspectives on personalized digital games for digital citizenship education: A PLS-SEM approach 数字化公民教育中个性化数字游戏的可持续性视角建模：PLS-SEM方法

Computers and Education Artificial Intelligence Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1016/j.caeai.2025.100498

Patcharin Panjaburee , Gwo-Jen Hwang , Ungsinun Intarakamhang , Niwat Srisawasdi

{"title":"Modeling the sustainability perspectives on personalized digital games for digital citizenship education: A PLS-SEM approach","authors":"Patcharin Panjaburee , Gwo-Jen Hwang , Ungsinun Intarakamhang , Niwat Srisawasdi","doi":"10.1016/j.caeai.2025.100498","DOIUrl":"10.1016/j.caeai.2025.100498","url":null,"abstract":"<div><div>As digital citizenship becomes an essential educational priority in the digital age, there is a growing need for sustainable and engaging instructional designs that foster students' ethical and responsible use of technology. Addressing this gap, this study modeled the sustainability perspectives underlying personalized digital game-based learning through a partial least squares structural equation modeling (PLS-SEM) approach. A longitudinal repeated-measures design was conducted with 372 lower secondary students in Thailand, using fuzzy logic and decision tree algorithms to personalize ethical digital scenarios. The proposed model examined how pedagogical design, content quality, usability, behavioral decisions, and motivation shape students' perceptions of sustainability. Results indicated that sustained motivation at later learning stages was the strongest predictor of perceived sustainability, while pedagogical and experiential factors exerted significant indirect effects through motivational engagement. The analysis also confirmed the longitudinal influence of early motivational experiences on later engagement, emphasizing the importance of adaptive feedback and reflective learning processes. These findings advance understanding of how AI-driven personalization can promote sustainable digital citizenship learning by integrating adaptive pathways, culturally relevant content, and motivational scaffolds to support long-term behavioral change. Implications for educational design, pedagogy, and policy are discussed to guide the development of scalable AI-supported learning environments.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100498"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effectiveness of Artificial Intelligence (AI) in language teaching 人工智能（AI）在语言教学中的有效性

Computers and Education Artificial Intelligence Pub Date : 2025-12-01 DOI: 10.1016/j.caeai.2025.100522

Peter Joseph Torres, Yunus Emre Kahveci

{"title":"Effectiveness of Artificial Intelligence (AI) in language teaching","authors":"Peter Joseph Torres, Yunus Emre Kahveci","doi":"10.1016/j.caeai.2025.100522","DOIUrl":"10.1016/j.caeai.2025.100522","url":null,"abstract":"<div><div>This study examines the effectiveness of artificial intelligence (AI) in language teaching, particularly in English as a Foreign Language (EFL) classrooms, following AI's increased adoption after the COVID-19 pandemic. Through a multilevel meta-analysis of 117 effect sizes across 46 empirical studies published between 2022 and 2025, results show that AI has a statistically significant medium-to-large overall impact on language learning (<em>g</em> = 0.74, 95 % CI [0.57, 0.92], <em>p</em> < .001) across all five major skills, with vocabulary showing the strongest effects, followed by reading, writing, listening, and speaking.</div><div>Grounded in constructivist, adaptive learning, and cognitive load theories, moderator analyses revealed several key insights: (1) AI is more effective in face-to-face and blended settings than in fully online classrooms; (2) AI is particularly effective for younger K-12 learners, suggesting tools are pedagogically optimized for foundational language learning; (3) similar effectiveness outcomes across AI platforms suggest implementation matters more than the tool itself; (4) AI can facilitate task completion but not develop long-term autonomous learning habits like self-regulation; and (5) AI works best as a supplement rather than replacement for traditional teaching.</div><div>The results, when interpreted through the novelty effect, cognitive load theory, and attention economy frameworks, suggest a technology saturation effect: the failure of AI tools to capture distinctive attention, reduce cognitive burden, or secure focused engagement in an already technology-rich environment. The synthesis outlines the current state of EFL literature, which has been focused on Asia and the Middle East, offering practical insights for educators considering AI integration.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100522"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the academic outcome of AI-powered joint support for at-risk students 评估人工智能为高危学生提供联合支持的学术成果

Computers and Education Artificial Intelligence Pub Date : 2025-12-01 Epub Date: 2025-11-30 DOI: 10.1016/j.caeai.2025.100517

Ning Xu , Fengjun Wu , Zhiyuan Liu , Yisi Zhan

{"title":"Evaluating the academic outcome of AI-powered joint support for at-risk students","authors":"Ning Xu , Fengjun Wu , Zhiyuan Liu , Yisi Zhan","doi":"10.1016/j.caeai.2025.100517","DOIUrl":"10.1016/j.caeai.2025.100517","url":null,"abstract":"<div><div>Despite the growing global adoption of AI-powered academic advice, its impact remains underexplored. This study addresses this gap by evaluating the academic performance of at-risk students who receive AI-driven joint support. We proposed a practical framework for assessing academic outcomes by leveraging AI as a decision-making tool for advisors. Our study employs a quasi-experimental difference-in-differences (DID) methodology over three semesters. We compare the academic outcomes of at-risk students in pilot departments who received the intervention (treatment group, <em>N</em> = 516) with all other students in the analytic sample (control group, <em>N</em> = 12,004), supplemented with robustness checks. The results revealed significant positive outcomes associated with AI-powered joint support, including a reduced proportion of at-risk students and higher grade point average (GPA). Specifically, students guided through the collaborative Center for Student Learning and Development (CSLD) in pilot departments achieved an average GPA increase of 0.4 points compared to their peers who were not supported. This approach not only enhances advisors' efficiency but also provides actionable insights into how AI-powered interventions can help at-risk students overcome academic challenges. Implications for advisors and program managers are discussed, emphasizing the potential for scalable data-driven academic support solutions.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conceptualizing AI literacies for children and youth: A systematic review on the design of AI literacy educational programs 儿童和青少年人工智能素养的概念化：人工智能素养教育项目设计的系统回顾

Computers and Education Artificial Intelligence Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 10.1016/j.caeai.2025.100491

Osnat Atias, Areej Mawasi

{"title":"Conceptualizing AI literacies for children and youth: A systematic review on the design of AI literacy educational programs","authors":"Osnat Atias, Areej Mawasi","doi":"10.1016/j.caeai.2025.100491","DOIUrl":"10.1016/j.caeai.2025.100491","url":null,"abstract":"<div><div>The growing presence of Artificial Intelligence (AI) in society increases the exposure of children and youth to these technologies. In response, recent research introduced educational programs that foster AI knowledge and competencies, collectively comprising AI literacy. This study presents a systematic review of 23 articles published up to 2023 describing AI literacy programs for children and youth. We examined: (1) motivations for teaching AI literacy, (2) conceptualizations of AI literacy that informed program design, and (3) learning theories and pedagogical methods employed. The analysis identified five motivational themes: workforce, informed users, purposeful creators, advocacy, and social good. Seventeen AI literacy frameworks and conceptual models were identified and grouped into four themes: competency-based, computational, sociotechnical, and practice-based. Application of a three-dimensional model of literacy (operational, sociocultural, and critical), shows that the operational dimension predominates in both frameworks and program designs, the sociocultural dimension is less accentuated, and the critical dimension is least evident. Cognitive constructivism emerged as the dominant learning theory guiding program design, often supported by hands-on activities and project-based learning methods. This systematic review advances understanding of the conceptual drivers shaping AI literacy programs for children and youth. The findings highlight the need for stronger conceptualizations of sociocultural and critical AI literacies and for their more balanced integration into educational programs. Addressing these gaps would better support broad motivations for teaching AI to children and youth, such as fostering social and ethical understanding and agency, and guide future research towards more comprehensive and critically informed frameworks.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"9 ","pages":"Article 100491"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0