How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study
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Abstract (arXiv)
Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 21 of its 79 references that are in the map. First posted on arXiv 17 Feb 2023.
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21 of 79 references.
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Citing papers per year: 2023: 5; 2024: 8; 2025: 3.
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- 2023Gender bias and stereotypes in Large Language Modelscited by 185
- 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
- 2024A Complete Survey on LLM-based AI Chatbotscited by 71
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- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directionscited by 18
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- 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awarenesscited by 14
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- 2024Had enough of experts? Quantitative knowledge retrieval from large language modelscited by 4
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- 2024A Misleading Gallery of Fluid Motion by Generative Artificial Intelligencecited by 3
- 2025Generative AI for Research Data Processing: Lessons Learnt From Three Use Casescited by 2
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- 2024ChatGPT in Research and Education: Exploring Benefits and Threatscited by 1
- 2025AI Chatbots as Professional Service Agents: Developing a Professional Identitycited by 1
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- 2024A Misleading Gallery of Fluid Motion by Generative Artificial Intelligence24 May 2024
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- 2024Had enough of experts? Quantitative knowledge retrieval from large language models12 Feb 2024
- 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness26 Jan 2024
- 2024Design Principles for Generative AI Applications25 Jan 2024
- 2023Gender bias and stereotypes in Large Language Models28 Aug 2023
4 more
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions26 Jul 2023
- 2023ChatGPT is a Remarkable Tool -- For Experts2 Jun 2023
- 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models4 Apr 2023
- 2023Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing27 Mar 2023
- 2023Gender bias and stereotypes in Large Language Modelscited by 185
- 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
- 2024A Complete Survey on LLM-based AI Chatbotscited by 71
- 2024Design Principles for Generative AI Applicationscited by 57
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directionscited by 18
- 2023ChatGPT is a Remarkable Tool -- For Expertscited by 16
- 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awarenesscited by 14
- 2023Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processingcited by 11
- 2024Had enough of experts? Quantitative knowledge retrieval from large language modelscited by 4
- 2024Protected group bias and stereotypes in Large Language Modelscited by 3
- 2024A Misleading Gallery of Fluid Motion by Generative Artificial Intelligencecited by 3
- 2025Generative AI for Research Data Processing: Lessons Learnt From Three Use Casescited by 2
12 most cited of 15 All 15 (JSON)
- 2025Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases22 Apr 2025
- 2025Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation20 Mar 2025
- 2024ChatGPT in Research and Education: Exploring Benefits and Threats5 Nov 2024
- 2024A Complete Survey on LLM-based AI Chatbots17 Jun 2024
- 2024ChatISA: A Prompt-Engineered, In-House Multi-Modal Generative AI Chatbot for Information Systems Education13 Jun 2024
- 2024A Misleading Gallery of Fluid Motion by Generative Artificial Intelligence24 May 2024
- 2024Protected group bias and stereotypes in Large Language Models21 Mar 2024
- 2024Had enough of experts? Quantitative knowledge retrieval from large language models12 Feb 2024
- 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness26 Jan 2024
- 2024Design Principles for Generative AI Applications25 Jan 2024
- 2023Gender bias and stereotypes in Large Language Models28 Aug 2023
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions26 Jul 2023
12 newest of 15 All 15 (JSON)