## 8 Future Work and New Research Directions  
While this research has demonstrated the potential of LLM models to dynamically adjust difficulty in a gamified logistics environment, it is important to acknowledge certain limitations that should be addressed in future studies:  
* Diversity of evaluated profiles: The study focused on a specific group of users, without considering a wide range of profiles with varying levels of experience and training in logistics. Evaluating LLM performance with a more diverse sample would validate their adaptability to different learning styles and competency levels.
* Exploration of other language models: The research was limited to a specific set of LLM models (Grok, ChatGPT, and DeepSeek). Alternatives such as LLaMA (Meta) or other emerging models, which could offer improvements in adaptability, efficiency, and personalization of the learning experience, were not assessed.
* Application to other warehouse tasks: The study concentrated on order picking, without extending its analysis to other key areas of the logistics environment, such as receiving, material sorting, or inventory management. Exploring the applicability of LLMs to these processes would allow for an evaluation of their impact across a broader range of warehouse operations.  
Recognizing these limitations provides a foundation for future research and optimizations, enabling the enhancement of LLM integration in logistical environments and expanding their applicability to more complex and realistic scenarios. Future research should focus on refining the integration of LLMs into gamified logistics environments, improving their ability to personalize the learning experience based on real-time user performance. One of the primary aspects to optimize is the adjustment of error tolerance and the adaptive scalability of difficulty, ensuring smoother and more effective progression. Likewise, enhancing the structure and timing of visual and contextual aids could increase the effectiveness of the training process. Another relevant line of research is the use of virtual and mixed reality environments in combination with LLM models to enhance immersion and learning effectiveness. Simulation in a more realistic setting could improve the transfer of knowledge to practical scenarios, optimizing training in logistical environments. Another key direction for future research is the application of this system in a real-world setting, transferring the dynamics developed in the game to an operational warehouse with actual order-picking processes. This approach would allow for an evaluation of the impact of AI-driven gamification under real working conditions, validating its effectiveness in optimizing logistical tasks. Furthermore, incorporating a collaborative multiplayer environment within the game could strengthen learning through user cooperation, fostering teamwork dynamics and problem-solving in gamified logistics settings. Addressing these areas will contribute to enhancing the role of AI-powered gamification in workforce training, making logistics learning more efficient, engaging, and tailored to the individual needs of each user.  
Study conception and design: [PERSON], [PERSON], [PERSON]; development, testing, and data collection: [PERSON]; analysis and interpretation of results: [PERSON], [PERSON], [PERSON]; [PERSON] wrote the manuscript with support from [PERSON] and [PERSON]. All authors reviewed the results and approved the final version of the manuscript.  
This research was funded by the Spanish Ministry of Science and Innovation through a research project with reference PID2022-139187 OB-I00.  
Not applicable.  
Data Availability Statement: Data are available in a publicly accessible repository at [[https://doi.org/10.5281/zenodo.15002938](https://doi.org/10.5281/zenodo.15002938)]([https://doi.org/10.5281/zenodo.15002938](https://doi.org/10.5281/zenodo.15002938)), accessed on 10 March 2025.  
The authors declare no conflict of interest.