Article: Transformation of the network and new media talent cultivation paradigm based on reinforcement learning and knowledge tracing from the perspective of human-machine collaboration Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.31 pp.1 - 21 Abstract: This paper addresses the issues of knowledge tracing and path optimisation in personalised learning path recommendation and proposes an intelligent educational model based on reinforcement learning and self-attention knowledge tracing (RL-SAKT). The model is built upon a self-attention knowledge tracing framework, incorporating reinforcement learning strategy optimisation, a multi-task learning mechanism, and a joint loss function. First, it models the students' long-term knowledge mastery through the self-attention mechanism, dynamically adjusting the learning path. Second, it introduces a personalised path recommendation strategy driven by reinforcement learning, optimising teaching interventions based on students' real-time performance and learning needs. Finally, it jointly optimises the knowledge tracing loss and reinforcement learning loss to improve prediction accuracy and learning efficiency. Experimental results show that the proposed RL-SAKT model outperforms traditional methods. Subjective evaluation results also show that this method has significant advantages in predicting students' learning progress, knowledge mastery, and optimising learning effectiveness. Inderscience Publishers - linking academia, business and industry through research

Title: Transformation of the network and new media talent cultivation paradigm based on reinforcement learning and knowledge tracing from the perspective of human-machine collaboration

Authors: Shan Cai; Lan Hu

Addresses: School of Art Design and Media, Wuhan Huaxia Institute of Technology, Wuhan 430223, China ' New Media Operation Department, Weiguan Vision (Shenzhen) Education Consulting Co, Shenzhen 518000, China

Abstract: This paper addresses the issues of knowledge tracing and path optimisation in personalised learning path recommendation and proposes an intelligent educational model based on reinforcement learning and self-attention knowledge tracing (RL-SAKT). The model is built upon a self-attention knowledge tracing framework, incorporating reinforcement learning strategy optimisation, a multi-task learning mechanism, and a joint loss function. First, it models the students' long-term knowledge mastery through the self-attention mechanism, dynamically adjusting the learning path. Second, it introduces a personalised path recommendation strategy driven by reinforcement learning, optimising teaching interventions based on students' real-time performance and learning needs. Finally, it jointly optimises the knowledge tracing loss and reinforcement learning loss to improve prediction accuracy and learning efficiency. Experimental results show that the proposed RL-SAKT model outperforms traditional methods. Subjective evaluation results also show that this method has significant advantages in predicting students' learning progress, knowledge mastery, and optimising learning effectiveness.

Keywords: reinforcement learning; self-attention; knowledge tracing; personalised learning path; talent cultivation.

DOI: 10.1504/IJICT.2026.152858

International Journal of Information and Communication Technology, 2026 Vol.27 No.31, pp.1 - 21

Received: 14 Nov 2025
Accepted: 31 Dec 2025
Published online: 13 Apr 2026 *

Title: Transformation of the network and new media talent cultivation paradigm based on reinforcement learning and knowledge tracing from the perspective of human-machine collaboration

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