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

• URL: http://arxiv.org/abs/2603.25925v1
• Date: Thu, 26 Mar 2026 21:31:10 GMT
• Title: Personalizing Mathematical Game-based Learning for Children: A Preliminary Study
• Abstract summary: Game-based learning (GBL) is widely adopted in mathematics education.<n>We propose a framework, guided by adaptive learning theory, that uses artificial intelligence (AI) techniques to build a classifier for player-generated levels.<n>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.
• Score: 3.9039335134518254
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models [16.77073947783477]
  This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines.<n>Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create, customize, and deploy LLM-powered game agents.
  arXiv  Detail & Related papers  (2026-04-23T17:46:29Z)
• GIFT: Games as Informal Training for Generalizable LLMs [64.47890325824763]
  Large Language Models (LLMs) struggle with "practical wisdom" and generalizable intelligence.<n>This gap arises from a lack of informal learning, which thrives on interactive feedback rather than goal-oriented instruction.<n>We propose treating Games as a primary environment for LLM informal learning, leveraging their intrinsic reward signals and abstracted complexity.
  arXiv  Detail & Related papers  (2026-01-09T08:42:44Z)
• People use fast, flat goal-directed simulation to reason about novel problems [68.55490343866545]
  We show that people are systematic and adaptively rational in how they play a game for the first time.<n>We explain these capacities via a computational cognitive model that we call the "Intuitive Gamer"<n>Our work offers new insights into how people rapidly evaluate, act, and make suggestions when encountering novel problems.
  arXiv  Detail & Related papers  (2025-10-13T15:12:08Z)
• ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation [3.333383360927007]
  This paper proposes ChatPCG, a large language model (LLM)-driven reward design framework. It leverages human-level insights, coupled with game expertise, to generate rewards tailored to specific game features automatically. ChatPCG is integrated with deep reinforcement learning, demonstrating its potential for multiplayer game content generation tasks.
  arXiv  Detail & Related papers  (2024-06-07T08:18:42Z)
• Instruction-Driven Game Engines on Large Language Models [59.280666591243154]
  The IDGE project aims to democratize game development by enabling a large language model to follow free-form game rules. We train the IDGE in a curriculum manner that progressively increases the model's exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, a universally cherished card game.
  arXiv  Detail & Related papers  (2024-03-30T08:02:16Z)
• WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models [91.92346150646007]
  In this work, we introduce WinoGAViL: an online game to collect vision-and-language associations. We use the game to collect 3.5K instances, finding that they are intuitive for humans but challenging for state-of-the-art AI models. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills.
  arXiv  Detail & Related papers  (2022-07-25T23:57:44Z)
• Unsupervised Hebbian Learning on Point Sets in StarCraft II [12.095363582092904]
  We present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method.
  arXiv  Detail & Related papers  (2022-07-13T13:09:48Z)
• Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning [27.593497502386143]
  Theory-Based Reinforcement Learning uses human-like intuitive theories to explore and model an environment. We instantiate the approach in a video game playing agent called EMPA. EMPA matches human learning efficiency on a suite of 90 Atari-style video games.
  arXiv  Detail & Related papers  (2021-07-27T01:38:13Z)
• AI in (and for) Games [0.9920773256693857]
  This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. On one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour. On the other hand, games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour.
  arXiv  Detail & Related papers  (2021-05-07T08:57:07Z)
• Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision tree [55.41644538483948]
  We present a new machine learning model by imitation based on the linguistic description of complex phenomena. The method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.
  arXiv  Detail & Related papers  (2021-01-06T21:14:10Z)
• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game [71.24825724518847]
  We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
  arXiv  Detail & Related papers  (2020-04-08T14:11:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.