[{"data":1,"prerenderedAt":46},["ShallowReactive",2],{"article-detail-journal":3,"article-detail:93":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":29,"author_ids":-1,"issue":38,"page_start":-1,"page_end":-1,"view_count":42,"download_count":43,"published_date":-1,"created_at":44,"funds":45},93,"Teaching through Making: AI, 3D Printing, and Social Manufacturing for Education","AI is rapidly reshaping educational practice, both on the consumption side (large-language-model tutors, AI-augmented classrooms) and on the production side (text-to-CAD generators, parameter recommenders, vision-based print monitors, automated documentation). Maker education, the strand of education that asks learners to build artifacts that physically work, is most directly affected when AI absorbs the operational layer of making. The key difficulty this raises is organizational: the educationally substantive layer of making is a residue of judgment at points of tension (with demand, with the physical world, with materials and process, with time and resources), and these tension sources lie outside the learner’s own control, so no single classroom can supply them at the rate the judgment layer requires. The difficulty decomposes into four organizational shortfalls (failure visibility, demand diversity, evaluation multiplicity, iteration continuity), each of which a single makerspace under-delivers. We propose Teaching through Making: a framework that rewrites the social-manufacturing objective function for an educational context, derives the four organizational conditions, and maps each to a combination of ACP-based mechanisms (artificial systems, computational experiments, parallel execution) over a five-layer resource architecture (infrastructure, platform, content, intelligence, governance). A crossed-factor computational experiment establishes that the toolkit raises the four indicators above single-classroom baseline in a controlled setting, with positive evidence on the depth indicators (evaluation tension, chain depth) and qualified evidence on the breadth indicators (failure exposure, demand diversity); empirical validation in real classrooms is identified as the natural next step.","regular",[22,23,24,25,26,27,28],"Maker education","social manufacturing","ACP framework","parallel intelligence","AI in education","3D printing","learning analytics",[30],{"id":31,"display_name":32,"first_name":33,"middle_name":-1,"last_name":34,"orcid":-1,"avatar":-1,"email":-1,"affiliation":-1,"bio":-1,"created_at":35,"updated_at":35,"affiliations":36,"articles":37},39,"Ge Jingwei","Jingwei","Ge","",[],[],{"id":39,"volume_number":13,"issue_number":13,"title":-1,"cover_image":40,"publish_date":-1,"is_current":41},30,"\u002Fuploads\u002Fissues\u002F30\u002F1f286088a0b24004a9e30ce512a717fe_793322e057af85236e9ebe1e6871a262.png",true,81,2,"2026-05-27T07:46:06.854044Z",[],1780657178664]