Generating Educational Game Levels with Multistep Deep Convolutional Generative Adversarial Networks

Abstract

Educational games offer significant potential for supporting personalized learning in engaging virtual worlds. However, many educational games do not provide adaptive gameplay to meet the needs of individual students. To address this issue, educational games should include game levels that can self-adjust to the specific needs of individual students. However, creating a large number of adaptable game levels requires considerable effort by game developers. A promising solution to this problem is to leverage procedural content generation to automatically generate levels for educational games that incorporate the desired learning objectives. In this paper, we propose a multistep deep convolutional generative adversarial network for generating new levels within a game for middle school computer science education. The model operates in two phases: (1) train a generator with a small set of human-authored example levels and generate a much larger set of synthetic levels to augment the training data for a second generator, and (2) train a second generator using the augmented training data and use it to generate novel educational game levels with enhanced solvability. We evaluate the performance of the model by comparing the novelty and solvability of generated levels between the two generators. Results suggest that the proposed multistep model significantly enhances the solvability of the generated levels with only minor degradation in the novelty of the generated levels.