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Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for
Can Zhao1, Jianzhong Zhu1, Jianhui Liu1
1Cangzhou Medical College, No. 39, Jiuhe West Road, Yunhe District, Cangzhou, Hebei Province, 061001, China.
Background:
Human Anatomy and Histology & Embryology are foundational courses in nursing education but are often challenging for students due to their complex spatial structures, dense knowledge points, and high cognitive load. Recent advances in generative artificial intelligence (AI) and knowledge graph technologies provide new opportunities for enhancing medical education. This study aimed to evaluate the effectiveness of a blended teaching model integrating a generative AI-powered digital tutor with a knowledge graph in improving learning outcomes among nursing students.
Methods:
A three-arm parallel randomized controlled trial was conducted among 362 first-year nursing students at Cangzhou Medical College, of whom 301 completed the study. Participants were randomly assigned to an AI-Enhanced Group, a Blended Teaching Group, or a Traditional Teaching Group. All groups followed the same 16-week course structure based on Gagné's Nine Events of Instruction, differing only in the teaching tools and tutoring strategies used. Learning outcomes were evaluated using module examination scores, final comprehensive scores, knowledge graph comprehension scores, knowledge retention rates, case-based inference accuracy, and multidimensional questionnaires. Statistical analyses were conducted using one-way ANOVA with post-hoc comparisons.
Results:
Significant differences were observed among the three groups across multiple learning outcomes. The AI-Enhanced Group achieved higher module scores (82.5 ± 7.3) compared with the Blended Group (77.6 ± 8.1) and Traditional Teaching Group (74.1 ± 8.5) (F = 28.64, p < 0.001). Final comprehensive scores were also significantly higher in the AI-Enhanced Group (84.2 ± 6.8) (F = 32.16, p < 0.001). Knowledge graph comprehension scores showed a large effect size (F = 103.72, p < 0.001). Furthermore, the AI-Enhanced Group demonstrated higher knowledge retention rates (82.3%) and case-based inference accuracy (91.2%) than the comparison groups. Questionnaire results indicated stronger recognition of digital tutors, higher acceptance of blended learning, and improved autonomous learning ability.
Conclusions:
The blended teaching model integrating a generative AI-powered digital tutor with a knowledge graph significantly improved nursing students' academic performance, knowledge retention, and clinical reasoning ability. This AI-enhanced instructional model may provide an effective approach for reforming foundational medical education and promoting structured knowledge learning.
