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Research on Generative AI-Enhanced English Major Teaching from the Perspective of Knowledge Co-Construction: A Case Study of the Course Survey of Major English-Speaking Countries

Author(s)

Jiangna Huang

Affiliation(s)

School of Foreign Languages, Lanzhou University of Arts and Science, Lanzhou, Gansu, China

Abstract

The booming advancement of generative artificial intelligence (GAI) has brought unprecedented development opportunities, yet also introduced practical dilemmas for the teaching innovation of undergraduate English majors. Based on the theoretical framework of knowledge co-construction, this paper explores the theoretical basis and practical implementable strategies of applying generative AI to English discipline teaching practice. Taking the core course Survey of Major English-Speaking Countries as the research object, this study constructs a human-machine collaborative teaching system featuring “three dimensions, four domains and five stages” to support interactive knowledge co-construction between teachers, students and intelligent technologies. This research elaborates on the internal operating logic of the innovative teaching model from three core levels, including the restructuring of classroom interactive subjects, the scenario-based optimisation of teaching design, and the staged cultivation of students’ comprehensive thinking abilities. Different from the traditional view that regards AI as a simple teaching aid, this study argues that generative AI ought to be positioned as an intelligent collaborative partner in modern teaching scenarios. By promoting in-depth cooperation in curriculum content optimisation, classroom activity innovation, multi-angle teaching evaluation and educational ethical standard formulation, the GAI-supported teaching mode can effectively drive the transformative upgrading of traditional English teaching. It helps shift the single knowledge-centered teaching paradigm to a competency-driven teaching orientation, which focuses on fostering students’ cross-cultural cognition, critical thinking and comprehensive practical abilities. Furthermore, this paper targets prominent practical difficulties in current AI-assisted English teaching, including insufficient technical accessibility, the shortage of teachers’ AI teaching application capabilities, blurred academic integrity boundaries and imperfect teaching quality supervision mechanisms, and puts forward corresponding feasible countermeasures and optimisation schemes.

Keywords

Co-Construction; Generative AI Technology; English Undergraduate Teaching; Curriculum Reform and Innovation; Human-Machine Teaching Synergy

References

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