A study on the impact of Generative Artificial Intelligence supported Situational Interactive Teaching on students’ ‘flow’ experience and learning effectiveness — a case study of legal education in China

Datos Bibliográficos

ID21484899
AutoresS J Shi (0000-0003-1682-6416, Beijing University of Posts and Telecommunications), Songhua Shi (Beijing University of Posts and Telecommunications), Jinkai Li (0000-0002-2257-211X, Beijing University of Posts and Telecommunications, autor de correspondencia), J W Li (0000-0003-4898-7601, Beijing University of Posts and Telecommunications), Rui Zhang (0000-0003-0194-5295, Beijing University of Posts and Telecommunications)
Año2024
Volumen44
Número1
Páginas112-138
Fecha de publicación2024-01-02
Open AccessNo
TipoARTICLE
RevistaAsia Pacific Journal of Education (JOURNAL)
Identificadores de la revistaISSN: 0218-8791 • E-ISSN: 1742-6855
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/02188791.2024.2305161
OpenAlexW4390913478
IdiomaEN
Citas recibidas15
Referencias citadas31

The rapid advancement of Generative Artificial Intelligence Technology has increasingly drawn attention to its potential applications in the educational sector. This study aims to investigate the effects of Situational Interactive Teaching, facilitated by generative artificial intelligence, on students’ learning outcomes and flow experiences. A series of experiments were designed to compare the performance of a Generative Artificial Intelligence-supported Situational Interactive Teaching Method with a Traditional Video Interactive Teaching Method. Data was collected using research tools such as questionnaires and test questions to assess students’ cognitive levels, learning effectiveness, flow experiences, and subjective evaluations during the instructional process. The analysis revealed distinct differences between the two teaching methods. The findings suggest that compared to traditional teaching methods, Generative Artificial Intelligence-supported Situational Interactive Teaching significantly improves students’ learning outcomes in cognitive, skill, and affective domains, while also enhancing flow experiences. These positive effects are not limited by individual student differences, indicating broad applicability. Furthermore, this teaching approach can foster a positive feedback loop between learning effectiveness and flow experience. In conclusion, this study confirms the effective application of generative artificial intelligence technology in legal education, providing empirical evidence for the promotion of this innovative teaching model in the educational field

Cognition · Generative grammar · Mathematics education · Situational ethics · Teaching method · Artificial Intelligence Applications · Artificial Intelligence in Law · Computer Science · Psychology · Social Psychology · Artificial Intelligence