{"title":"AI for data generation in education: Towards learning and teaching support at scale","authors":"Mohammad Khalil, Qinyi Liu, Jelena Jovanovic","doi":"10.1111/bjet.13580","DOIUrl":null,"url":null,"abstract":"<p>Supporting learning and teaching at scale requires access to large and high-quality content and datasets for analysis and innovation. With rapid advances in artificial intelligence (AI) and the growing demand for data, synthetic data has emerged as a potential solution for addressing these challenges. This editorial introduces the contributions of five accepted articles to the special section AI for Synthetic Data Generation in Education: Scaling Teaching and Learning. These articles explore key themes in leveraging AI-generated synthetic data to support learning and teaching as well as enhance educational practices at scale. The editorial emphasizes that hybrid strategies that leverage AI alongside human judgment are essential for scaling support for learning and teaching through synthetic data generation.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 3","pages":"993-998"},"PeriodicalIF":6.7000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13580","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Educational Technology","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/bjet.13580","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
Supporting learning and teaching at scale requires access to large and high-quality content and datasets for analysis and innovation. With rapid advances in artificial intelligence (AI) and the growing demand for data, synthetic data has emerged as a potential solution for addressing these challenges. This editorial introduces the contributions of five accepted articles to the special section AI for Synthetic Data Generation in Education: Scaling Teaching and Learning. These articles explore key themes in leveraging AI-generated synthetic data to support learning and teaching as well as enhance educational practices at scale. The editorial emphasizes that hybrid strategies that leverage AI alongside human judgment are essential for scaling support for learning and teaching through synthetic data generation.
期刊介绍：
BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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