Article: Personalised learning path generation mechanism based on RL and knowledge graph Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.8 pp.74 - 93 Abstract: This article proposes a personalised learning path generation mechanism that combines reinforcement learning and knowledge graphs. It constructs a knowledge graph that includes knowledge points, student status, and historical behaviour. Using this image and learning trajectory, it constructed a student model. Then, the RL algorithm optimises the learning path based on real-time feedback. An experiment targeting 300 college students compared the proposed method with traditional methods. The results showed that reinforcement learning based methods improved learning outcomes by 12.5%, increased learning satisfaction by 23.7%, accelerated knowledge acquisition by 15%, and shortened average learning time by 8%. These findings confirm the effectiveness of this mechanism in improving learning outcomes and meeting individual student needs. Inderscience Publishers - linking academia, business and industry through research

Title: Personalised learning path generation mechanism based on RL and knowledge graph

Authors: Zhao Liu; Yanan Shang

Addresses: Cangzhou Normal University, Cangzhou, 061000, China ' Cangzhou Normal University, Cangzhou, 061000, China

Abstract: This article proposes a personalised learning path generation mechanism that combines reinforcement learning and knowledge graphs. It constructs a knowledge graph that includes knowledge points, student status, and historical behaviour. Using this image and learning trajectory, it constructed a student model. Then, the RL algorithm optimises the learning path based on real-time feedback. An experiment targeting 300 college students compared the proposed method with traditional methods. The results showed that reinforcement learning based methods improved learning outcomes by 12.5%, increased learning satisfaction by 23.7%, accelerated knowledge acquisition by 15%, and shortened average learning time by 8%. These findings confirm the effectiveness of this mechanism in improving learning outcomes and meeting individual student needs.

Keywords: personalised learning; reinforcement learning; knowledge graph; learning pathways; data analysis.

DOI: 10.1504/IJICT.2026.151597

International Journal of Information and Communication Technology, 2026 Vol.27 No.8, pp.74 - 93

Received: 27 Sep 2025
Accepted: 29 Oct 2025
Published online: 09 Feb 2026 *

Title: Personalised learning path generation mechanism based on RL and knowledge graph

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