A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety
This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and prelim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11586 |
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| author | Andrea Falegnami Andrea Tomassi Giuseppe Corbelli Francesco Saverio Nucci Elpidio Romano |
| author_facet | Andrea Falegnami Andrea Tomassi Giuseppe Corbelli Francesco Saverio Nucci Elpidio Romano |
| author_sort | Andrea Falegnami |
| collection | DOAJ |
| description | This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow, iterative cycles of empirical data collection and analysis, which can be both time-intensive and costly. In contrast, our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse, realistic data sets on demand. This capability allows for more flexible and accelerated experimentation, enhancing the efficiency and scalability of safety science research. By detailing an application case, we demonstrate the practical implementation and advantages of our framework, such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies. |
| format | Article |
| id | doaj-art-c631d7b4f177438eaa8682876937d77c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c631d7b4f177438eaa8682876937d77c2024-12-27T14:07:45ZengMDPI AGApplied Sciences2076-34172024-12-0114241158610.3390/app142411586A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and SafetyAndrea Falegnami0Andrea Tomassi1Giuseppe Corbelli2Francesco Saverio Nucci3Elpidio Romano4Management Engineering Faculty, Uninettuno University, 00186 Rome, ItalyManagement Engineering Faculty, Uninettuno University, 00186 Rome, ItalyPsychology Faculty, Uninettuno University, 00186 Rome, ItalyResearch and Innovation Area, Uninettuno University, 00186 Rome, ItalyManagement Engineering Faculty, Uninettuno University, 00186 Rome, ItalyThis paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow, iterative cycles of empirical data collection and analysis, which can be both time-intensive and costly. In contrast, our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse, realistic data sets on demand. This capability allows for more flexible and accelerated experimentation, enhancing the efficiency and scalability of safety science research. By detailing an application case, we demonstrate the practical implementation and advantages of our framework, such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies.https://www.mdpi.com/2076-3417/14/24/11586OHSChatGPTAIchain of thoughtcomplexitydesign science research |
| spellingShingle | Andrea Falegnami Andrea Tomassi Giuseppe Corbelli Francesco Saverio Nucci Elpidio Romano A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety Applied Sciences OHS ChatGPT AI chain of thought complexity design science research |
| title | A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety |
| title_full | A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety |
| title_fullStr | A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety |
| title_full_unstemmed | A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety |
| title_short | A Generative Artificial-Intelligence-Based Workbench to Test New Methodologies in Organisational Health and Safety |
| title_sort | generative artificial intelligence based workbench to test new methodologies in organisational health and safety |
| topic | OHS ChatGPT AI chain of thought complexity design science research |
| url | https://www.mdpi.com/2076-3417/14/24/11586 |
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