[{"data":1,"prerenderedAt":49},["ShallowReactive",2],{"article-detail-journal":3,"article-detail:90":16},["Reactive",4],{"title":5,"description":6,"cover_image":7,"overview":8,"issn":9,"publisher":10,"publishing_mode":11,"impact_factor":9,"impact_factor_5year":9,"submission_to_decision_days":9,"downloads":12,"id":13,"created_at":14,"updated_at":15},"Artificial Intelligence for Education","Artificial Intelligence for Education is a peer-reviewed, open-access, interdisciplinary journal focusing on the development, application, evaluation, and governance of artificial intelligence in education. The journal serves as a scholarly platform for research that connects AI technologies with educational theory, learning science, pedagogy, assessment, policy, and institutional innovation.\n\nThe journal welcomes studies that combine technical rigor with educational significance. It encourages submissions that not only propose AI methods or systems, but also explain how such approaches improve learning quality, teaching effectiveness, educational equity, learner development, or institutional decision-making.\n\nThrough its open-access model, Artificial Intelligence for Education aims to promote global knowledge sharing and support responsible, inclusive, and human-centered AI innovation in education.","\u002Fuploads\u002Fjournals\u002F1\u002Fd6013241332a4908bec5d8b2135e3e47_ai4edu_cover_image.png","\u003Cp>\u003Cstrong>Journal Title: \u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Artificial Intelligence for Education\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Journal Type:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Peer-reviewed, open-access, interdisciplinary journal\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Aim and Mission:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">Artificial Intelligence for Education is a peer-reviewed, open-access journal dedicated to advancing research, practice, and innovation at the intersection of artificial intelligence and education. The journal aims to provide a professional platform for scholars, educators, engineers, and practitioners to publish high-quality work on AI-enabled learning, teaching, assessment, governance, and educational transformation.\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Scope:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>The journal welcomes submissions in areas including, but not limited to:\u003C\u002Fp>\n\u003Cp>• AI-supported teaching and learning\u003Cbr>\n• Intelligent tutoring systems and educational agents\u003Cbr>\n• Learning analytics and educational data mining\u003Cbr>\n• Human–AI collaboration in education\u003Cbr>\n• Large language models and generative AI for education\u003Cbr>\n• AI ethics, governance, fairness, privacy, and safety in education\u003Cbr>\n• Personalized, adaptive, and lifelong learning systems\u003Cbr>\n• AI for K–12, higher education, vocational education, and lifelong learning\u003Cbr>\n• Smart classrooms, digital learning environments, and educational platforms\u003Cbr>\n• AI-assisted curriculum design, assessment, feedback, and evaluation\u003Cbr>\n• Educational robotics, virtual reality, and immersive learning\u003Cbr>\n• Policy, management, and institutional transformation in AI education\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Article Types:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Research Articles, Review Articles, Perspective Articles, Communications and Letters, Editorials.\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Open Access Policy:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>This journal is a Gold Open Access journal. All published articles are freely and permanently available online immediately upon publication. Readers may read, download, copy, distribute, and link to the full text of articles, subject to the applicable Creative Commons license.\u003C\u002Fp>\n\u003Cp>The journal currently does not charge article processing charges (APCs). Any future APC policy will be clearly disclosed on the journal website.\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Copyright and License:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">Authors retain the copyright of their work. Articles published in Artificial Intelligence for Education are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing free use, sharing, distribution, reproduction, and adaptation in any medium, provided that proper attribution is given. This policy supports open knowledge exchange and responsible reuse of research for AI-driven educational innovation.\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Publisher:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Metaverse Learning Press LLC\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Sponsoring \u002F Affiliated Organization:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Association of Global Intelligent Science and Technology\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>Publication Frequency:\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Quarterly, 4 issues per year.\u003C\u002Fp>",null,"Association of Global Intelligent Science and Technology","Gold Open Access",0,1,"2026-05-14T04:29:06.553512Z","2026-05-19T13:55:06.843980Z",{"id":17,"title":18,"abstract":19,"type":20,"doi":-1,"keywords":21,"authors":32,"author_ids":-1,"issue":41,"page_start":-1,"page_end":-1,"view_count":45,"download_count":46,"published_date":-1,"created_at":47,"funds":48},90,"Smart Journey Deploying AI Agents for K12+ Students","Education at every level, from K-12 through university, confronts an irreducible structural barrier: one human instructor cannot provide personalized, real-time attention to every student who needs it. When a student ventures beyond familiar material and becomes stuck, she enters a queue for instructor help. As class size grows, the queue lengthens, and the expected cost of exploration eventually exceeds its expected benefit. Rational students abandon active learning for safe, superficial routines. This is not a motivational failure. It is a congestion game. It scales with every student added to the classroom. We formalize the traditional classroom as a Constrained Exploration Game (CEG) and prove that Smart Journey’s AI Agent deployment converts an unstable congestion trap into a stable exploration equilibrium. Three theorems establish: (i) a congestion threshold beyond which exploration collapses in AI-free classrooms; (ii) an attention restoration condition showing that even a modest number of AI Agents restores exploration at any scale; and (iii) a regret bound demonstrating an order-of-magnitude improvement in learning efficiency. Simulations across class sizes from 20 to 500 students confirm a twenty-seven-fold improvement in concept mastery with AI Agents deployed. An ablation experiment decomposes AI’s contribution into structural and cognitive components, finding that 83%–97% of the total learning gain comes from structural unbundling of attention. The central finding is that AI Agents do not need to teach better than humans to transform education at scale. They only need to teach without queuing.","regular",[22,23,24,25,26,27,28,29,30,31],"Congestion games","mechanism design for education","exploration-exploitation","AI agents","LLM-powered tutoring","Smart Journey","making by learning","K12+ education","queueing theory","regret analysis",[33],{"id":34,"display_name":35,"first_name":36,"middle_name":-1,"last_name":37,"orcid":-1,"avatar":-1,"email":-1,"affiliation":-1,"bio":-1,"created_at":38,"updated_at":38,"affiliations":39,"articles":40},32,"Guan Sangtian","Sangtian","Guan","",[],[],{"id":42,"volume_number":13,"issue_number":13,"title":-1,"cover_image":43,"publish_date":-1,"is_current":44},30,"\u002Fuploads\u002Fissues\u002F30\u002F1f286088a0b24004a9e30ce512a717fe_793322e057af85236e9ebe1e6871a262.png",true,103,7,"2026-05-21T03:29:15.596542Z",[],1780657178664]