Adaptive historical education through Generative AI and Immersive Game Design

  • Meisam Taheri
  • , Magdalena Cyma-Wejchenig
  • , Lucia Gomes
  • , Kevin Tan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study presents the design and development of a 2D narrative game set in a 3D environment, leveraging Artificial Intelligence (AI) to deliver interactive age-tailored and educational historical storytelling. The game combines 2D-style narratives with 3D settings to create an immersive historical experience. Players interact with a companion character powered by Large Language Model Meta AI (Llama), an advanced language model, at key checkpoints. The AI delivers historically accurate content, adapting its storytelling to different age groups (children, teens, and adults) to ensure accessibility and sustained engagement. The game promotes interactive learning by integrating gameplay with period-specific historical narratives. To ensure accuracy, Llama is configured to respond strictly within the context of each historical period. Players can engage in dialogue with the AI for context-appropriate guidance, supported by a Python-based API that dynamically adjusts narratives based on player progress and age. This study explores the integration of Natural Language Processing (NLP) in games to enhance educational experiences, showcasing how age-adaptive storytelling and interactive dialogue improve historical learning through gameplay. The study details the game’s architecture and its balance of academic content with immersive gameplay. This innovative approach highlights AI’s transformative potential in digital learning.

Original languageEnglish
Title of host publicationProceedings of the Future Technologies Conference, FTC 2025, Volume 1
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages85-99
Number of pages15
ISBN (Print)9783032079855
DOIs
Publication statusPublished - 2026
EventFuture Technologies Conference, FTC 2025 - Munich, Germany
Duration: 6 Nov 20257 Nov 2025

Publication series

NameLecture Notes in Networks and Systems
Volume1675 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceFuture Technologies Conference, FTC 2025
Country/TerritoryGermany
CityMunich
Period6/11/257/11/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Funding

This study is based on the work of COST Action CA22145-GameTable [17], supported by COST (European Cooperation in Science and Technology).

Keywords

  • Adaptive learning
  • Gamification
  • Generative artificial intelligence
  • Historical education
  • Immersive educational game
  • Natural language processing

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