<scp>AI</scp> for data generation in education: Towards learning and teaching support at scale
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.
Authors
- Qinyi Liu (ORCID: https://orcid.org/0009-0003-4973-0901)
- Mohammad Khalil (ORCID: https://orcid.org/0000-0002-6860-4404)
- Jelena Jovanovic (ORCID: https://orcid.org/0009-0001-0639-8908)
Institutions
- University of Belgrade (RS)
- University of Bergen (NO)
Publication Details
- Journal
- British Journal of Educational Technology
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1111/bjet.13580
- Citations
- 6
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 10.16