Article: Reinforcement learning empowered RNN: exploring personalised learning path optimisation in nursing education Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.94 pp.65 - 89 Abstract: Personalised learning paths are critical in nursing education, yet traditional algorithms struggle to adapt to students' dynamic cognitive states. To address this, this study proposes a novel framework integrating recurrent neural networks (RNN) and reinforcement learning (RL). The RNN continuously encodes temporal learning histories to capture evolving knowledge states. Subsequently, the RL agent utilises these latent states to formulate dynamic learning paths, maximising long-term clinical skill retention via a custom reward function. Extensive experiments on a real-world nursing dataset demonstrate that our RL-RNN framework achieves an AUC-ROC of 0.812 and an average cumulative reward of 19.3, outperforming state-of-the-art predictive and RL baselines. Furthermore, it significantly reduces the average instructional steps required to reach clinical mastery to 31.8. Ablation and case studies validate the framework's practical viability for dynamic curriculum generation, ensuring efficient medical training. Inderscience Publishers - linking academia, business and industry through research

Title: Reinforcement learning empowered RNN: exploring personalised learning path optimisation in nursing education

Authors: Mingyue Fang

Addresses: School of Nursing, Xinyang Vocational and Technical College, Xinyang, 464000, China

Abstract: Personalised learning paths are critical in nursing education, yet traditional algorithms struggle to adapt to students' dynamic cognitive states. To address this, this study proposes a novel framework integrating recurrent neural networks (RNN) and reinforcement learning (RL). The RNN continuously encodes temporal learning histories to capture evolving knowledge states. Subsequently, the RL agent utilises these latent states to formulate dynamic learning paths, maximising long-term clinical skill retention via a custom reward function. Extensive experiments on a real-world nursing dataset demonstrate that our RL-RNN framework achieves an AUC-ROC of 0.812 and an average cumulative reward of 19.3, outperforming state-of-the-art predictive and RL baselines. Furthermore, it significantly reduces the average instructional steps required to reach clinical mastery to 31.8. Ablation and case studies validate the framework's practical viability for dynamic curriculum generation, ensuring efficient medical training.

Keywords: reinforcement learning; RL; recurrent neural networks; RNN; personalised learning path; nursing education; intelligent tutoring systems; knowledge tracing.

DOI: 10.1504/IJICT.2026.156128

International Journal of Information and Communication Technology, 2026 Vol.27 No.94, pp.65 - 89

Received: 17 Mar 2026
Accepted: 16 May 2026
Published online: 04 Sep 2026 *

Title: Reinforcement learning empowered RNN: exploring personalised learning path optimisation in nursing education

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