Romero Marras, Juan JoséTorre Cubillo, Luis de laChaos García, Dictino2025-06-102025-06-102025-06-06Romero Marras, J.J.; De la Torre, L.; Chaos García, D. WarehouseGame Training: A Gamified Logistics Training Platform Integrating ChatGPT, DeepSeek, and Grok for Adaptive Learning. Appl. Sci. 2025, 15, 6392. https://doi.org/10.3390/app151263922076-3417https://doi.org/10.3390/app15126392https://hdl.handle.net/20.500.14468/26881The registered version of this article, first published in “Applied Sciences, vol. 15, 2025", is available online at the publisher's website: MDPI, https://doi.org/10.3390/app15126392 La versión registrada de este artículo, publicado por primera vez en “Applied Sciences, vol. 15, 2025", está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/app15126392Modern warehouses play a fundamental role in today’s logistics, serving as strategic hubs for the reception, storage, and distribution of goods. However, training warehouse operators presents a significant challenge due to the complexity of logistics processes and the need for efficient and engaging learning methods. Training in logistics operations requires practical experience and the ability to adapt to real-world scenarios, which can result in high training costs. In this context, gamification and artificial intelligence emerge as innovative solutions to enhance training by increasing operator motivation, reducing learning time, and optimizing costs through personalized approaches. But is it possible to effectively apply these techniques to logistics training? This study introduces WarehouseGame Training, a gamified training tool developed in collaboration with Mecalux Software Solutions and implemented in Unity 3D. The solution integrates large language models (LLMs) such as ChatGPT, DeepSeek, and Grok to enhance adaptive learning. These models dynamically adjust challenge difficulty, provide contextual assistance, and evaluate user performance in logistics training scenarios. Through this gamified training tool, the performance of these AI models is analyzed and compared, assessing their ability to improve the learning experience and determine which one best adapts to this type of training.eninfo:eu-repo/semantics/openAccess1203.17 Informáticaartículogamificationlogistics traininglarge language modelsadaptive learningUnity 3DChatGPTDeepSeekGrok