This paper describes an AI model designed to provide human-like guidance and tutoring for spatial reasoning skills within a block-puzzle game.

In plain terms

Spatial reasoning abilities are crucial for STEM fields, and games offer an engaging way to develop these skills in children. To make these games effective for learning, they need Artificial Intelligence (AI) that can provide human-like tutoring and guidance. This paper introduces a hybrid qualitative model designed for Camelot Jr., a block-puzzle game where players build multi-level bridges. Qualitative reasoning (QR) models use symbolic representations, allowing the AI to "think" in ways that can be easily translated into understandable feedback for players. To ensure the model accurately handles the game's physics, they integrated a mathematical center-of-mass stability logic into the qualitative solver. This work facilitates spatial skill training within the game and contributes to creating human-centric, explainable AI agents for educational purposes.