TY - GEN
T1 - Data Generation in Blended Learning
T2 - 19th International Conference on Blended Learning, ICBL 2026
AU - Paoprasert, Naraphorn
AU - Chui, Kwok Tai
AU - Liu, Jiaqi
AU - Chi, Hao Ran
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2027.
PY - 2027
Y1 - 2027
N2 - Blended learning has received ever-growing attention and adoption in recent years, initially and mainly driven by the pandemic. Students, parents, and teachers have shared many fruitful teaching, learning, and caring experiences of blended learning. Data generation (e.g., large language models) is a cutting-edge technology believed to be a breakthrough in artificial intelligence applications. Bringing data generation into blended learning has become an emerging research topic; thus, this paper reports a first-of-its-kind review of recent developments. An advanced search of the Scopus Database was used to obtain initially 52 studies; after introducing exclusion criteria, 33 studies were shortlisted for analyses in this paper. Six aspects were included in the basic analyses, followed by advanced analyses to discuss the applications, methodologies, results, and insights of each study. As an emergent research area, this review provides ideas and research implications to drive future research directions.
AB - Blended learning has received ever-growing attention and adoption in recent years, initially and mainly driven by the pandemic. Students, parents, and teachers have shared many fruitful teaching, learning, and caring experiences of blended learning. Data generation (e.g., large language models) is a cutting-edge technology believed to be a breakthrough in artificial intelligence applications. Bringing data generation into blended learning has become an emerging research topic; thus, this paper reports a first-of-its-kind review of recent developments. An advanced search of the Scopus Database was used to obtain initially 52 studies; after introducing exclusion criteria, 33 studies were shortlisted for analyses in this paper. Six aspects were included in the basic analyses, followed by advanced analyses to discuss the applications, methodologies, results, and insights of each study. As an emergent research area, this review provides ideas and research implications to drive future research directions.
KW - Blended Learning
KW - Data Generation
KW - Educational Technology
KW - Hybrid Learning
KW - Large Language Models
UR - https://www.scopus.com/pages/publications/105046940728
U2 - 10.1007/978-981-92-3761-6_21
DO - 10.1007/978-981-92-3761-6_21
M3 - Conference contribution
AN - SCOPUS:105046940728
SN - 9789819237609
T3 - Lecture Notes in Computer Science
SP - 296
EP - 310
BT - Blended Learning. Innovations for Future Education - 19th International Conference on Blended Learning, ICBL 2026, Proceedings
A2 - Chui, Kwok Tai
A2 - Cheung, Simon K.S.
A2 - Lee, Fred Wang-Fat
A2 - Li, Chen
A2 - Jia, Jiyou
A2 - Shang, Junjie
Y2 - 28 July 2026 through 31 July 2026
ER -