AI Interactive Scaffolding on Self-Evaluation and Motivation in K–12 Self-Directed Speaking Learning

• In Event: Artificial Intelligence (AI) for Cognition and Learning

Abstract

Emerging research is highlighting the necessity of investigating artificial intelligence (AI) in K-12 self-directed learning. However, it remains under-explored regarding the effectiveness of AI interactive scaffolding on self-evaluation, motivation, and speaking learning. Thus, we conducted a randomized controlled experiment to examine the effectiveness. A total of 60 seventh graders participated in 10 sessions, with the treatment group interacting with an AI conversational agent, and the control group without interaction. Results revealed that the treatment group showed more learning gains in self-evaluation, motivation, and vocabulary. Moreover, self-evaluation was an important predictor for speaking performance, which was positively associated with the overall speaking scores and the sub-dimensions of fluency, vocabulary, and pronunciation. Finally, practical and research implications were revealed.

Authors

• Feifei Wang, The Chinese University of Hong Kong

• Xiaohua Zhou, Lingnan University

• Kangxin Li, Chinese University of Hong Kong

• Alan C. K. Cheung, The Chinese University of Hong Kong

• Ming Tian