Personalizing Mathematical Game-based Learning for Children: A Preliminary Study

2026-03-26

AI Summary

This study addresses the lack of effective mechanisms in current math gamification systems for automatically matching high-quality, personalized levels to children’s abilities. Drawing on adaptive learning theory, it introduces an AI-driven level classification framework into a children’s mathematics game for the first time. The approach leverages four machine learning models—Random Forest, Support Vector Machine, Decision Tree, and k-Nearest Neighbors—to extract features from and predict the educational efficacy of 206 user-generated levels created by experts and advanced players in a creative mode. Experimental results demonstrate that the Random Forest model achieves the best performance, validating that AI-assisted analysis of user-generated content can effectively support the construction of personalized learning pathways. This work thus offers a novel paradigm for the design of intelligent educational games.

Abstract

Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners' engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mechanisms to deliver them to appropriate players in a way that matches their learning abilities. To address this challenge, we propose a framework, guided by adaptive learning theory, that uses artificial intelligence (AI) techniques to build a classifier for player-generated levels. We collect 206 distinct game levels created by both experts and advanced players in Creative Mode, a new tool in a math game-based learning app, and develop a classifier to extract game features and predict valid game levels. The preliminary results show that the Random Forest model is the optimal classifier among the four machine learning classification models (k-nearest neighbors, decision trees, support vector machines, and random forests). This study provides insights into the development of GBL systems, highlighting the potential of integrating AI into the game-level design process to provide more personalized game levels for players.

Problem

• game-based learning
• personalization
• mathematics education
• adaptive learning
• player-generated content

Innovation

• game-based learning
• adaptive learning
• AI-powered level classification
• personalized education
• Random Forest