Article: Design and application of AI-based personalised learning evaluation systems: full-link optimisation from data collection to intelligent feedback Journal: International Journal of Systems, Control and Communications (IJSCC) 2026 Vol.17 No.7 pp.1 - 20 Abstract: The rapid pace of the evolution of artificial intelligence (AI) and its integration with information and communications technologies (ICT) has created the opportunities to conduct intelligent, adaptive and scalable student learning assessment. The present paper proposes the AI-Based Personalised Learning Evaluation System based on the Learning Path Optimisation and Feedback Intelligence Model (LPOFIM). The three layers consist of system architecture. Data Collection Layer is where heterogeneous information about the online and off-line learning setting is sent regarding academic performance data, behavioural engagement and cognitive process indicators. The Evaluation Layer applies hybrid AI techniques in the form of a combination of graph-based learner profiling and a deep learning classifier to reveal personal strengths, weaknesses, and style patterns in learning. Reinforcement learning drives the Feedback Layer to create adaptive learning paths and personalised recommendations. The proposed model promotes learner interest, retention and performance by providing individualised learning experiences. Inderscience Publishers - linking academia, business and industry through research

Title: Design and application of AI-based personalised learning evaluation systems: full-link optimisation from data collection to intelligent feedback

Authors: Yicheng Liu

Addresses: International College of Football, Tongji University, Shanghai, 200092, China

Abstract: The rapid pace of the evolution of artificial intelligence (AI) and its integration with information and communications technologies (ICT) has created the opportunities to conduct intelligent, adaptive and scalable student learning assessment. The present paper proposes the AI-Based Personalised Learning Evaluation System based on the Learning Path Optimisation and Feedback Intelligence Model (LPOFIM). The three layers consist of system architecture. Data Collection Layer is where heterogeneous information about the online and off-line learning setting is sent regarding academic performance data, behavioural engagement and cognitive process indicators. The Evaluation Layer applies hybrid AI techniques in the form of a combination of graph-based learner profiling and a deep learning classifier to reveal personal strengths, weaknesses, and style patterns in learning. Reinforcement learning drives the Feedback Layer to create adaptive learning paths and personalised recommendations. The proposed model promotes learner interest, retention and performance by providing individualised learning experiences.

Keywords: personalised learning; AI evaluation systems; full-link optimisation; reinforcement learning; adaptive feedback.

DOI: 10.1504/IJSCC.2026.155798

International Journal of Systems, Control and Communications, 2026 Vol.17 No.7, pp.1 - 20

Received: 16 Sep 2025
Accepted: 25 Dec 2025
Published online: 14 Aug 2026 *

Title: Design and application of AI-based personalised learning evaluation systems: full-link optimisation from data collection to intelligent feedback

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