[{"data":1,"prerenderedAt":45},["ShallowReactive",2],{"article-detail-journal":3,"article-detail:88":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":28,"author_ids":-1,"issue":37,"page_start":-1,"page_end":-1,"view_count":41,"download_count":42,"published_date":-1,"created_at":43,"funds":44},88,"Building CASE by Generative AI Making Cases Out of Control AutomationScience and Engineering","Control and Automation Science and Engineering (CASE) is often taught through a small set of familiar examples: inverted pendulums, cruise controllers, tank-level systems, and similar benchmark problems. These examples are useful, but they no longer reflect the diversity and pace of modern automation practice. Developing new cases is difficult because it requires control-theoretic expertise, domain knowledge, access to realistic operating data, and repeated pedagogical calibration. This paper introduces CASE-Gen, a generative-AI-assisted framework for making teaching cases out of control and automation source materials. The framework follows three steps: scenario extraction from technical sources, schema-guided case structuring, and multi-dimensional quality assurance. Unlike direct prompting, CASE-Gen constrains generation through a CASE-specific schema and verifies outputs using retrieval grounding, Bloom’s taxonomy checks, difficulty calibration, and symbolic consistency tests. We describe the design rationale, implementation workflow, an illustrative case package, and an evaluation plan, then show how the framework can support accreditation-oriented curriculum development, professional upskilling, and cross-institutional case sharing. The paper is a framework proposal rather than a completed empirical evaluation. The central argument is modest but practical: generative AI should not replace expert case authors, but it can turn the most time-consuming parts of case development into a supervised, reviewable workflow.","regular",[22,23,24,25,26,27],"Generative AI","CASE education","case-based learning","control engineering education","large language models","retrieval-augmented generation",[29],{"id":30,"display_name":31,"first_name":32,"middle_name":-1,"last_name":33,"orcid":-1,"avatar":-1,"email":-1,"affiliation":-1,"bio":-1,"created_at":34,"updated_at":34,"affiliations":35,"articles":36},29,"Tian Yonglin","Yonglin","Tian","",[],[],{"id":38,"volume_number":13,"issue_number":13,"title":-1,"cover_image":39,"publish_date":-1,"is_current":40},30,"\u002Fuploads\u002Fissues\u002F30\u002F1f286088a0b24004a9e30ce512a717fe_793322e057af85236e9ebe1e6871a262.png",true,109,8,"2026-05-21T03:01:23.656736Z",[],1780657178664]