Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment
Daniele Rege Cambrin, Gabriele Scaffidi Militone, Luca Colomba, Giovanni Malnati, Daniele Apiletti, Paolo Garza

TL;DR
This paper introduces an automated method using Vision-Language Models to evaluate and improve the quality of game tutorials by analyzing tutorial videos and providing feedback, reducing manual testing efforts.
Contribution
It presents a novel VLM-based approach for automated game tutorial assessment, enabling efficient detection of confusing scenes and potential errors.
Findings
VLMs can effectively analyze game tutorial frames
The approach reduces manual testing time
It improves tutorial quality assessment accuracy
Abstract
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…
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Taxonomy
TopicsEducational Games and Gamification · Intelligent Tutoring Systems and Adaptive Learning
