Abstract

With the rapid advancement of educational digitalization and generative artificial intelligence, knowledge graphs and AI agents have emerged as key technologies supporting smart higher education. To address challenges in current AI-assisted teaching, including insufficient support from structured knowledge systems, inadequate coordination across teaching processes, and delayed evaluation and feedback, this study proposes a collaborative framework integrating knowledge graphs and AI agents within an integrated teaching platform, and illustrates its integrated operational mechanism for knowledge organization, learning support, learning analytics, and teaching evaluation. Using the core course Nautical Navigation in the Navigation Technology Specialty at WHUT as a case study, the framework was implemented on the Chaoxing Smart Course Platform and applied to 459 students across two cohorts. The implementation achieved knowledge structuring and learning process visualization. The constructed course knowledge graph includes 336 knowledge points and more than 2700 associated learning resources and assessment items, while 30 instructional AI agents were developed and deployed to support different teaching and learning scenarios. The results indicate that the framework improves course knowledge organization, enhances student engagement and self-directed learning, enables visualization of learning processes and precision in teaching evaluation, and promotes a shift from experience-based to data-driven instructional decision-making. Compared with the previous cohort, students’ average daily learning time increased from 564 s to 618 s, participation rates in chapter quizzes, group discussions, and assignment completion all exceeded 90%, and more than 80% of respondents expressed willingness to continue using this learning model. The study provides a practical reference for AI-enabled teaching reform and digital transformation in higher education.

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