Structure and process: A theoretical model of intelligent elementary Chinese vocabulary teaching

Abstract

With the rapid development of artificial intelligence technology, Chinese vocabulary teaching is gradually entering an era of innovation, offering vast potential for more intelligent and personalized teaching models. Existing research mostly focuses on the auxiliary role of AI technology in second language teaching, with less attention given to the implementation of intelligent second language teaching. To address this issue, this study, based on the perspective of Artificial Intelligence Generated Content (AIGC), attempts to construct an elementary Chinese vocabulary teaching (ECVT) theoretical model, presenting the development process of an ECVT system. From the existing research literature on Chinese vocabulary teaching, a total of 17 viewpoints on teaching structures and 19 viewpoints on teaching processes for ECVT are outlined. Based on this, the study first establishes the fundamental macro-level phases of ECVT, then delves into the micro-level structures and processes of each phase, ultimately deducing a theoretical model for ECVT oriented towards intelligent teaching. Integrating the perspectives of artificial intelligence deconstruction and generation into the research on Chinese vocabulary teaching not only offers front-line teachers a reference for optimizing teaching models but also provides a more forward-looking and scientifically grounded framework for Chinese language teaching, even other language teaching. This hence propels innovation in second language teaching models.