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

Abstract

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.