Construction Strategy for an AI Digital Human Multimodal Interactive Teaching Model in the “Introduction to Business” Knowledge Graph Course

Abstract

With the in-depth advancement of the national education digitalization strategy, the integrated development of artificial intelligence and higher education teaching has become a key direction of curriculum reform in universities. The traditional Introduction to Business curriculum suffers from fragmented knowledge systems, single teaching modes, insufficient personalized education, and delayed learning situation feedback, which can hardly meet the training demands for high-quality applied talents in business English majors in the new era. Based on the Treenity platform with the knowledge graph of “Introduction to Business” course in Zhejiang Yuexiu University, this study focuses on the integrated application of AI digital human multimodal interaction technology and curriculum teaching. Firstly, it sorts out the domestic and foreign research status of AI digital humans and knowledge graphs in the education field, and identifies the research gaps in their in-depth integration. Secondly, it systematically elaborates the architectural framework, knowledge hierarchy and ability training system of the knowledge graph-based Introduction to Business curriculum. Finally, an AI digital human multimodal interactive teaching mode with dual cores of AI teacher assistant and AI smart learning partner is constructed, refining the full-scenario application functions including lesson preparation assistance, in-class teaching, after-class tutoring and personalized learning. This study establishes a complete human-machine collaborative teaching system and a practical teaching implementation path, which effectively makes up for the shortcomings of traditional curriculum teaching and realizes targeted teaching and personalized education. It provides practical references for the digital teaching reform of economics, management and business courses in universities, and offers new ideas and paradigms for the innovative iteration of human-machine collaborative teaching modes under the background of educational digitalization.