###### Abstract  
Modern 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. gamification; logistics training; large language models; adaptive learning; Unity 3D; ChatGPT; DeepSeek; Grok +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-0000-00000-0000-00  
activities, such as order picking and inventory management, can lead to lower motivation. The self-determination theory (SDT) emphasizes that intrinsic motivation flourishes when people feel competent, autonomous, and socially connected [6]. This is where training tools can significantly aid operators during their learning journeys. In recent times, a variety of technologies have emerged to improve learning, including serious games, gamification, and artificial intelligence. Serious games are digital solutions specifically designed for educational and training purposes, intertwining training goals with engaging gameplay, leading to increased adoption in educational environments due to their ability to enhance learning across multiple subjects [7; 8; 9]. Serious games actively involve users through engaging and challenging interactions, prompting many organizations to implement them for employee training in areas like leadership, sales, or vital operations. Another significant technology is gamification, which applies game-like elements and mechanics in settings outside of gaming, such as business and education [10]. When gamification is incorporated into training tools, it boosts user engagement, motivation, and learning results. Lastly, artificial intelligence, especially the use of large language models (LLMs), is a key change agent in education and gamification. LLMs are sophisticated neural networks built on extensive textual datasets, designed to comprehend, generate, and adapt language in context. By using architectures like transformers and attention mechanisms, these models can understand queries, produce coherent replies, and flexibly adjust information based on user interactions. Within this platform, LLM-based tools like ChatGPT 4, DeepSeek, and Grok [11; 12; 13] can enhance personalization in gamification and serious games. These models can fluidly adjust challenges and content within the platform, tailoring them to fit the skills and requirements of the players [14; 15]. This research aimed to assess and contrast the performance of the leading AI models available today to find out which one best fits gamification environments.