Article: AI-powered recommendation and task assignment mechanism for interactive vocational English teaching Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.11 pp.1 - 17 Abstract: This study presents an AI-powered recommendation and task assignment mechanism designed to enhance interactive vocational English teaching. Making use of NLP, machine learning methods, and massive language models, the system personalises learning by analysing student proficiency, learning styles, and task performance. The proposed framework incorporates modules for content creation, personalised learning, and adaptive recommendations, supported by features such as passage and video wizards. Data collected from 500 students across rural and urban areas was processed to generate tailored learning paths, with performance evaluated using various regression models. Results indicate that the Huber Regress or achieved the highest predictive accuracy, enabling dynamic adjustments to learning tasks. The system demonstrated improved engagement and learning outcomes, particularly in contexts promoting learner-generated content and autonomy. These results demonstrate the promise of AI-powered platforms to provide practical, scalable language instruction. Inderscience Publishers - linking academia, business and industry through research

Title: AI-powered recommendation and task assignment mechanism for interactive vocational English teaching

Authors: Yanxia Geng; Meilan Jin

Addresses: Department of Basic Courses, Shanghai Vocational College of Agriculture and Forestry, Shanghai, 201600, China ' School of Foreign Languages, Huzhou University, Huzhou, 313000, China

Abstract: This study presents an AI-powered recommendation and task assignment mechanism designed to enhance interactive vocational English teaching. Making use of NLP, machine learning methods, and massive language models, the system personalises learning by analysing student proficiency, learning styles, and task performance. The proposed framework incorporates modules for content creation, personalised learning, and adaptive recommendations, supported by features such as passage and video wizards. Data collected from 500 students across rural and urban areas was processed to generate tailored learning paths, with performance evaluated using various regression models. Results indicate that the Huber Regress or achieved the highest predictive accuracy, enabling dynamic adjustments to learning tasks. The system demonstrated improved engagement and learning outcomes, particularly in contexts promoting learner-generated content and autonomy. These results demonstrate the promise of AI-powered platforms to provide practical, scalable language instruction.

Keywords: artificial intelligence; recommendation system; task assignment; vocational English teaching; ML; personalised learning; educational technology; learner-generated context.

DOI: 10.1504/IJICT.2026.151714

International Journal of Information and Communication Technology, 2026 Vol.27 No.11, pp.1 - 17

Received: 20 Aug 2025
Accepted: 02 Dec 2025
Published online: 16 Feb 2026 *

Title: AI-powered recommendation and task assignment mechanism for interactive vocational English teaching

Keep up-to-date