Talk · International Conference on Electrical, Computer and Energy Technologies · July 1, 2026

KNN-Based Personalized Book Recommendation Model for Schoolchildren Using Bilingual Datasets and Gamified Learning Environment

Aray M. Kassenkhan, Arman T. Sholpanbayev

Abstract

This paper presents the development of an intelligent bilingual book recommendation model for schoolchildren, based on the K-Nearest Neighbors (KNN) algorithm and integrated into an educational platform supporting both Kazakh and Russian languages. The system personalizes reading suggestions by combining content- and user-based features, such as book metadata (title, author, genre, age group, language, description) and learner profiles (age, gender, language of instruction, preferred genres, reading history). The model is trained on open-source datasets and enhanced with real user feedback, including post-reading ratings and engagement metrics. To boost motivation and long-term engagement, the platform incorporates gamification elements-points, levels, badges, and reading challenges-alongside AI-driven personalization. Experimental results show high recommendation accuracy and user satisfaction, with low computational overhead. The approach supports bilingual literacy, critical thinking, and reading motivation through individualized learning pathways, contributing to the modernization of Kazakhstan's educational system.

Recommender Systems and TechniquesOnline Learning and AnalyticsLiteracy and Educational Practicesbook recommendationselementary school studentsk-nearest neighbors

KNN-Based Personalized Book Recommendation Model for Schoolchildren Using Bilingual Datasets and Gamified Learning Environment

Abstract

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