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Seamless Tutorial: Contextual State Transition Generation Based on Player Internal Knowledge

delete2025-12-01
delete0
PRE
AI
I
In-Chang Baek
T
Taehwa Park
K
Kyung-Joong Kim *
DOI：10.1109/TG.2025.3596055delete
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Abstract

Abstract

En 中文
In the domain of game artificial intelligence, tutorial systems have seen limited advancement despite their critical role in onboarding players. Traditional tutorials often neglect individual learning differences, leading to ineffective instruction. This study proposes a personalized in-game tutorial generation framework that leverages procedural content generation and student modeling. The system integrates a Monte Carlo tree search-based state transition generator and a player modeling module to dynamically adapt tutorial content based on inferred internal knowledge. The approach is validated through large-scale user testing (N = 88) in a commercial-style Match-3 puzzle game environment. Results show that the personalized generator improves learning by up to 44.4% within short sessions. The findings highlight the potential of adaptive tutorials in accelerating skill acquisition and enhancing game experience through seamless, personalized learning contexts.
Keywords:
Games
Tutorials
Generators
Layout
Cloning
Timing
Procedural generation
Monte Carlo methods
Adaptation models
Video games
Game tutorial generation
Monte Carlo Tree Search (MCTS)
Procedural Content Generation (PCG)
state transition

Journal

I
IEEE Transactions on Games
IF:
2.8
Papers:
45
Citations:
0

Organization

No organization information available