Research on the practical path of knowledge graph empowering medical immunology teaching in colleges and universities under the background of educational digitalization and Intelligence

Objective To explore the practical path of integrating the knowledge graph technology in the field of artificial intelligence into the teaching of medical immunology under the background of digital and intelligent education, and design and construct an intelligent teaching optimization plan.

Methods A total of 61 sophomore students from two teaching classes of the Clinical Medicine major were selected, and divided into two groups. The observation group (31 cases) were given the intelligent teaching mode based on the knowledge graph, and the control group (30 cases) were given the traditional teaching mode. The theoretical examination scores, learning behavior data and questionnaire interview results between two groups were compared, and the quality and efficiency of classroom teaching were evaluated.

Results The quality score of learning path completion in the observation group was 4.27 ± 0.53 (full score: 5 points), and this score was significantly positively correlated with the frequency of platform usage (r = 0.63, P ＜ 0.01). The autonomous expansion behaviors in situational tasks (such as consulting cross-module literature or raising innovative questions), active search behaviors on the platform and reading volume of non-specified resources in the observation group were higher than those in control group (P ＜ 0.01). The access frequency of intelligent teaching platform in the observation group was higher than that in control group, and the single learning duration on the knowledge graph was also higher than that of reviewing the lecture notes and completing the homework in control group (P ＜ 0.01). The total score of teaching quality in the observation group was higher than that in control group (P ＜ 0.01). The 87.10% of the students (27/31) in the observation group believed that introducing the knowledge graph into the smart teaching model had a positive effect in improving classroom efficiency, optimizing the teaching structure and promoting the internalization of knowledge.

Conclusions Under the background of educational digitalization and intelligence, introducing artificial intelligence knowledge graph technology into the teaching of medical immunology is conducive to optimizing students' knowledge acquisition paths, improving teaching quality and efficiency, and is worthy of further promotion and application in smart medical education.