Currently submitted to: JMIR Medical Education
Date Submitted: Sep 24, 2026
Open Peer Review Period: Sep 25, 2026 - Nov 20, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development and Evaluation of a Knowledge Graph-Grounded AI-Assisted Learning System for Undergraduate Pediatric Nursing Education
ABSTRACT
Background:
Pediatric nursing education requires students to integrate developmental, biomedical, pharmacological, and nursing knowledge and apply these concepts to complex clinical situations. Knowledge graphs may provide structured representations of disciplinary knowledge, whereas artificial intelligence (AI) may support personalized and interactive learning.
Objective:
This study aimed to develop and evaluate a knowledge graph–grounded AI-assisted learning system for undergraduate Pediatric Nursing education.
Methods:
A quasi-experimental study using a historical control cohort was conducted among undergraduate nursing students enrolled in a Pediatric Nursing course at a university in China. Students enrolled in 2022 received conventional blended learning and constituted the historical control cohort, whereas students enrolled in 2023 received blended learning supported by a course-specific pediatric nursing knowledge graph and an AI teaching assistant. The system was developed using the Analysis, Design, Development, Implementation, and Evaluation framework and integrated structured knowledge representation, AI-assisted tutoring, formative assessment, personalized learning recommendations, and learning analytics. Outcomes included overall course performance, active learning, learning engagement, and intervention-related satisfaction.
Results:
A total of 341 students were included, comprising 178 students in the experimental cohort and 163 in the historical control cohort. Compared with the historical control cohort, the experimental group had higher overall course scores (80.08 ± 6.44 vs. 71.18 ± 10.27, P < 0.001) and higher active learning state (51.62 ± 9.56 vs. 47.05 ± 10.90, P = 0.004). Significant between-group differences were also observed in learning motivation, deep learning, learning regulation, and thorough learning. Overall learning engagement was higher in the intervention cohort (85.36 ± 15.47 vs. 80.05 ± 15.42, P = 0.025), primarily due to higher learning absorption. Satisfaction with the intelligent learning system was high among intervention participants (mean 85.57 ± 11.32).
Conclusions:
The knowledge graph- and AI-assisted intelligent learning system was associated with higher academic performance, active learning, and overall learning engagement compared with conventional blended learning. The findings provide preliminary evidence supporting the integration of structured knowledge representation and AI-assisted learning into undergraduate Pediatric Nursing education.
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