Article: AI-driven personalised English learning path planning algorithm and blended learning platform construction Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.9 pp.147 - 173 Abstract: In response to the contradiction between algorithm recommendation precision and learner autonomy in personalised English learning path planning and the problem of weak personalisation of blended learning platforms, this paper focuses on the AI-driven personalised English learning path planning algorithm and the construction of a blended learning platform. First, a knowledge graph is used to model the structure of English learning content and achieve semantic associations between knowledge points. Subsequently, a multi-dimensional learner profile is constructed by combining bidirectional encoder representations from transformers (BERT) and light gradient boosting machine (LightGBM) to precisely capture learning behaviours and characteristics. Finally, a deep Q-network (DQN) algorithm is applied to dynamically generate personalised learning paths based on learner status. At the same time, a blended learning system is built based on the Open EdX open source platform, and combined with the AI-driven personalised English learning path planning algorithm proposed in this paper, online AI and offline teaching are organically integrated. Experimental verification shows that this solution increases learner engagement by 55.45% compared to traditional teaching methods and improves overall autonomous learning ability by an average of 12.7%, meeting personalised needs and providing a new path for English teaching reform. Inderscience Publishers - linking academia, business and industry through research

Title: AI-driven personalised English learning path planning algorithm and blended learning platform construction

Authors: Shuling Yang; Dongqing Hao

Addresses: School of Foreign Languages, Jilin Normal University, Siping 136000, Jilin, China ' School of Foreign Language Studies, Inner Mongolia Medical University, Hohhot 010110, Inner Mongolia Autonomous Region, China

Abstract: In response to the contradiction between algorithm recommendation precision and learner autonomy in personalised English learning path planning and the problem of weak personalisation of blended learning platforms, this paper focuses on the AI-driven personalised English learning path planning algorithm and the construction of a blended learning platform. First, a knowledge graph is used to model the structure of English learning content and achieve semantic associations between knowledge points. Subsequently, a multi-dimensional learner profile is constructed by combining bidirectional encoder representations from transformers (BERT) and light gradient boosting machine (LightGBM) to precisely capture learning behaviours and characteristics. Finally, a deep Q-network (DQN) algorithm is applied to dynamically generate personalised learning paths based on learner status. At the same time, a blended learning system is built based on the Open EdX open source platform, and combined with the AI-driven personalised English learning path planning algorithm proposed in this paper, online AI and offline teaching are organically integrated. Experimental verification shows that this solution increases learner engagement by 55.45% compared to traditional teaching methods and improves overall autonomous learning ability by an average of 12.7%, meeting personalised needs and providing a new path for English teaching reform.

Keywords: artificial intelligence; blended learning; path planning algorithm; knowledge graph modelling; personalised English learning.

DOI: 10.1504/IJCEELL.2026.153607

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.9, pp.147 - 173

Received: 05 Aug 2025
Accepted: 16 Jan 2026
Published online: 18 May 2026 *

Title: AI-driven personalised English learning path planning algorithm and blended learning platform construction

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