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ChatGPT, AI-generated content, and engineering management

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Abstract

This study explores the integration of ChatGPT and AI-generated content (AIGC) in engineering management. It assesses the impact of AIGC services on engineering management processes, mapping out the potential development of AIGC in various engineering functions. The study categorizes AIGC services within the domain of engineering management and conceptualizes an AIGC-aided engineering lifecycle. It also identifies key challenges and emerging trends associated with AIGC. The challenges highlighted are ethical considerations, reliability, and robustness in engineering management. The emerging trends are centered on AIGC-aided optimization design, AIGC-aided engineering consulting, and AIGC-aided green engineering initiatives.

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References

  • Baidoo-Anu D, Owusu Ansah L (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1): 52–62

    Article  Google Scholar 

  • Blanchard B S (2004). System Engineering Management. Hoboken, NJ: John Wiley & Sons

    Google Scholar 

  • Cao Y, Li S, Liu Y, Yan Z, Dai Y, Yu P S, Sun L (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv preprint. arXiv:2303.04226

  • Cheng K, Neisch P, Cui T (2023). From concept to space: A new perspective on AIGC-involved attribute translation. Digital Creativity, 34(3): 211–229

    Article  Google Scholar 

  • Du H, Li Z, Niyato D, Kang J, Xiong Z, Shen X, Kim D I (2023). Enabling AI-generated content (AIGC) services in wireless edge networks. arXiv preprint. arXiv:2301.03220

  • Epstein Z, Hertzmann A, The Investigators of Human Creativity (2023). Art and the science of generative AI. Science, 380(6650): 1110–1111

    Article  ADS  CAS  PubMed  Google Scholar 

  • Gravel B E, Svihla V (2021). Fostering heterogeneous engineering through whole-class design work. Journal of the Learning Sciences, 30(2): 279–329

    Article  Google Scholar 

  • Guo B, Zhang X, Wang Z, Jiang M, Nie J, Ding Y, Yue J, Wu Y (2023). How close is ChatGPT to human experts? Comparison corpus, evaluation, and detection. arXiv preprint. arXiv:2301.07597

  • Jin Z (2023). Analysis of the technical principles of ChatGPT and prospects for pre-trained large models. In: 3rd International Conference on Information Technology, Big Data and Artificial Intelligence. Chongqing: IEEE, 1755–1758

    Google Scholar 

  • Jo A (2023). The promise and peril of generative AI. Nature, 614(1): 214–216

    Google Scholar 

  • Lv Y (2023). Artificial intelligence-generated content in intelligent transportation systems: Learning to copy, change, and create. IEEE Intelligent Transportation Systems Magazine, 15(5): 2–3

    Article  Google Scholar 

  • Nielsen J (1992). The usability engineering life cycle. Computer, 25(3): 12–22

    Article  Google Scholar 

  • Skibniewski M J (2014). Research trends in information technology applications in construction safety engineering and management. Frontiers of Engineering Management, 1(3): 246–259

    Article  Google Scholar 

  • Thorp H H (2023). ChatGPT is fun, but not an author. Science, 379(6630): 313–313

    Article  ADS  PubMed  Google Scholar 

  • van Dis E A M, Bollen J, Zuidema W, van Rooij R, Bockting C L (2023). ChatGPT: Five priorities for research. Nature, 614(7947): 224–226

    Article  ADS  CAS  PubMed  Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I (2017). Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA: Curran Associates Inc., 6000–6010

    Google Scholar 

  • Yang F, Wang M (2020). A review of systematic evaluation and improvement in the big data environment. Frontiers of Engineering Management, 7(1): 27–46

    Article  Google Scholar 

Download references

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Correspondence to Yeming Gong.

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Yu, Z., Gong, Y. ChatGPT, AI-generated content, and engineering management. Front. Eng. Manag. 11, 159–166 (2024). https://doi.org/10.1007/s42524-023-0289-6

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  • DOI: https://doi.org/10.1007/s42524-023-0289-6

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