Designing effective game tutorials is crucial for a smooth learning curve for new players, especially in games with many rules and complex core mechanics. Evaluating the effectiveness of these tutorials usually requires multiple iterations with testers who have no prior knowledge of the game. Recent Vision-Language Models (VLMs) have demonstrated significant capabilities in understanding and interpreting visual content. VLMs can analyze images, provide detailed insights, and answer questions about their content. They can recognize objects, actions, and contexts in visual data, making them valuable tools for various applications, including automated game testing. In this work, we propose an automated game-testing solution to evaluate the quality of game tutorials. Our approach leverages VLMs to analyze frames from video game tutorials, answer relevant questions to simulate human perception, and provide feedback. This feedback is compared with expected results to identify confusing or problematic scenes and highlight potential errors for developers. In addition, we publish complete tutorial videos and annotated frames from different game versions used in our tests. This solution reduces the need for extensive manual testing, especially by speeding up and simplifying the initial development stages of the tutorial to improve the final game experience.

翻译：设计有效的游戏教程对于新玩家实现平缓的学习曲线至关重要，尤其在规则繁多、核心机制复杂的游戏中。评估这些教程的有效性通常需要与对游戏无先验知识的测试者进行多轮迭代。近期，视觉语言模型（VLMs）在理解和解析视觉内容方面展现出显著能力。VLMs能够分析图像、提供详细洞察并回答关于图像内容的问题。它们能够识别视觉数据中的对象、动作和上下文，使其成为包括自动化游戏测试在内的多种应用的宝贵工具。在本研究中，我们提出一种自动化游戏测试方案，用于评估游戏教程的质量。我们的方法利用VLMs分析来自视频游戏教程的帧画面，通过回答相关问题来模拟人类感知并提供反馈。该反馈与预期结果进行比较，以识别令人困惑或有问题的场景，并为开发者突出显示潜在错误。此外，我们公开发布了测试中使用的完整教程视频及来自不同游戏版本的标注帧。该方案减少了对大量人工测试的需求，尤其通过加速和简化教程的初始开发阶段来提升最终游戏体验。