Abstract

The rapid integration of generative artificial intelligence (GenAI), particularly GenAI-powered conversational agents, into language learning is transforming pedagogical practices by providing learners with engaging, real-time learning experiences. Although GenAI-assisted speaking tasks offer unprecedented opportunities for oral practice, the role of instructional teacher feedback as an essential component of language teaching in shaping students’ task engagement remains relatively unexplored. This talk presents two studies examining how teacher feedback influences EFL learners’ speaking engagement over four rounds of AI-assisted argumentative tasks, with one focusing on speaking performance feedback and the other on critical AI literacy (CAIL) feedback. Data were collected through questionnaires and semi-structured interviews to capture changes in learners’ behavioral, cognitive, affective, and social engagement across task iterations. The talk concludes with insights into the design of practical feedback frameworks that foster learner engagement and thereby support the development of speaking performance.

Bio

Peijian Paul Sun (PhD, University of Auckland) is a professor in the Department of Linguistics at Zhejiang University. His research focuses on L2 speaking, foreign language education, and educational technology. His publications have appeared in journals, including TESOL Quarterly, Computer Assisted Language Learning, and Language Teaching Research, among others. He serves as a guest editor for System and a guest associate editor for Frontiers in Psychology. He is also a member of the editorial boards of System, Asia-Pacific Education Researcher, and Researching and Teaching Chinese as a Foreign Language.