Research on the Application of Knowledge Graph and AI Task Engine in Empowering Personalized Instruction for Fundamental Computer Science Courses

Abstract

Against the backdrop of the profound integration of professional accreditation in engineering education with the digitalization of education, computer-related foundational courses, serving as pivotal vehicles for talent development, generally confront salient challenges such as fragmented knowledge delivery, inadequate personalized instruction, an onerous teaching workload for educators, and a superficial infusion of ideological and political education into the curriculum. To tackle these pressing issues, this study leverages the sophisticated knowledge graph, AI task engine, multimedia educational resources, and question bank system of the Fanya Learning Platform, in conjunction with frontline teaching reform initiatives, to investigate a curriculum development pathway that seamlessly integrates AI technology with the knowledge graph while intertwining ideological and political education throughout the curriculum. Through the refinement of platform-based teaching materials, the construction of dynamic cognitive profiles for students, and the incorporation of ideological and political elements such as the spirit of craftsmanship and patriotism, a comprehensive closed-loop precision teaching and collaborative education model is established. This model offers theoretical insights and practical guidelines for the digital and ideological transformation of computer-related foundational courses, thereby facilitating the cultivation of high-caliber professionals endowed with both ethical integrity and professional prowess.