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

Abstract

ABSTRACT 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.