## 5 Conclusions  
This research represents a transformative advance in the field of OHS, providing a novel AI-driven workbench that not only enables the design and testing of innovative methodologies but also addresses long-standing challenges in early-stage method validation. By integrating LLMs and synthetic data generation tools, this framework creates a dynamic platform that can simulate and evaluate new OHS methodologies with unprecedented efficiency and rigour, effectively eliminating the practical and ethical constraints typically associated with real-world data collection. The applications of this approach are far-reaching: it provides OHS researchers and practitioners with a powerful tool for prototyping and refining methodologies, ultimately accelerating the adoption of data-driven safety interventions in industries where rapid, evidence-based responses are critical. Thisworkbench has the potential to fundamentally reshape safety science, enabling the iterative and responsive development of methodologies that can be tailored to different sectors and adapted to complex and evolving workplace environments. The implications of this research extend far beyond OHS, positioning it as a valuable model for other high-risk areas where accurate, context-specific data are difficult to obtain, such as healthcare, environmental monitoring, and disaster management. The ability to test new safety protocols in a simulated environment paves the way for more agile, reliable, and proactive risk management strategies, fostering a safer and more resilient organisational culture. Despite its strengths, there are limitations due to the synthetic nature of the data, which, while highly customisable, may lack the complete unpredictability and variability of real-world scenarios. In addition, the reliance on LLMs requires careful oversight to ensure that the outputs generated are accurately aligned with sector-specific requirements. It is also crucial to recognise the inherent biases present within all LLMs, which can be tracked back to the training stage in which the word embeddings are built. Since LLMs have to be trained on a large amount of textual data, it is very difficult to avoid the presence of biases inherited from the sources. This is a well-documented issue of such models that is currently a trending research topic both for industry and academia. This is another reason to keep the human in the loop and not to make the process fully automated. However, the authors want to warn the audience to take into account any potential discrepancy in AI-generated results that can lead to other biases where common knowledge is overemphasised while niche, specific knowledge pertinent to particular fields, such as safety sciences, is marginalized. To counteract these biases and enhance the quality and relevance of synthetic data, one might employ advanced techniques such as retrieval-augmented generation. This method, not addressed in this paper, could supplement the LLM with targeted, context-specific documents that enrich its understanding and output, ensuring a more balanced and accurate generation of synthetic data that are reflective of the specialised terminology and concepts critical to the OHS research domain. Future developments could focus on improving the realism of synthetic data models and extending the applicability of the framework to different high-risk domains. This research thus lays the foundation for a new era in occupational health and safety science, providing a scalable, adaptable, and scientifically robust tool that enables organisations to anticipate, assess, and mitigate risk with unprecedented precision and foresight. A.F. : Conceptualization, Methodology, Software, Data curation, Writing--original draft preparation, Results interpretation, Figures realization, Validation, Reviewing and Editing; A.T.: Conceptualization, Methodology, Software, Data curation, Writing--original draft preparation, Results interpretation, Figures realization, Validation, Reviewing and Editing; G.C.