Constructing an AI-Driven "Learning-Research-Application" Integrated Teaching Model for University History Courses: Innovation in Modern World History Based on Knowledge Graphs

Qidong Zhao and Wei Deng

Education Insights, 2025, vol. 2, issue 8, 73-88

Abstract: Integrating artificial intelligence (AI) technology into the entire teaching and learning process is an essential requirement for local universities to cultivate high-quality, application-oriented talents in teacher education programs. To align the curriculum with the needs of educational reform and development in the AI era, the teaching team has addressed students' pain points related to learning content, methods, and environments by constructing an AI-empowered "Learning-Research-Application" integrated learning system based on the "Objectives-Resources-Models-Activities-Assessment" framework. This system enhances the curriculum's higher-order thinking, challenge, and emotional engagement. By employing knowledge graphs and practical teaching methods empowered by intelligent digital technologies, the course improves students' participation, inquiry skills, and practical abilities. Additionally, a three-dimensional, five-component assessment system covering "Knowledge Mastery-Competence Development-Process Performance" activates students' autonomous learning, provides personalized evaluation of higher-order thinking, and achieves a virtuous cycle of "Learning-Research-Application". This study explores a new approach to fully integrating AI into the teaching process of knowledge graph-based courses, offering an innovative model for connected teaching and learning.

Keywords: AI-driven teaching model; knowledge graph; autonomous learning; teaching innovation; university history courses; scenario-based learning (search for similar items in EconPapers)
Date: 2025
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