Skip to main content
SpringerLink
Log in

Indicators of AI in Automation: An Evaluation Using Intuitionistic Fuzzy DEMATEL Method with Special Reference to Chat GPT

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Numerous examples have shown that huge language models are useful. OpenAI's ChatGPT is a conversational bot that can respond appropriately in human-like situations. Massive amounts of data were used in its training. As of this writing, ChatGPT is the most advanced chatbot in existence. Because it can produce high-quality writing in a matter of seconds, this chatbot has generated a lot of excitement—and some dire predictions—about its potential impact on higher education assessment and other fields. The widespread popularity and acceptance of ChatGPT is a fascinating phenomenon that attracts millions of users and viewers every day. ChatGPT that is difficult to complete is often disregarded or even hated by its intended audience. Therefore, it is essential to learn about the numerous factors that could affect the accuracy of ChatGPT. This research, however, focuses on the difficulties of ChatGPT and provides a thorough examination of them. We have isolated 12 problems and analysed their interrelationships. Using an approach developed at the Intuitive Fuzzy Decision Making and Trial Evaluation Laboratory (IF–DEMATEL), the identified problems are then partitioned into cause and effect categories for additional research. The first stage is a thorough examination, by multiple specialists, of the causal relationships between major obstacles. The evaluation results are shown as intuitive fuzzy numbers (IFN). The second step is to convert the lingo into IFN. Third, DEMATEL offers a structure for identifying issues and their constituent causes. IF–DEMATEL is found to be the most effective method for analyzing the interplay of several problems in ChatGPT when compared to other DEMATEL variants like classical DEMATEL and fuzzy DEMATEL. Successful identification of important obstacles that experts and project managers should focus on is greatly aided by the findings of this inquiry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of Data and Material

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Code Availability (Software Application or Custom Code)

Code for implementation are available on request due to privacy or other restrictions.

References

  1. Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries?. Library Hi Tech News.

  2. King, M. R., & chatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 1–2.

  3. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of Applied Learning and Teaching, 6(1).

  4. 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. Available at SSRN 4337484.

  5. Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198.

  6. Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. The Lancet Digital Health, 5(3), e105–e106.

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., Do, Q. V., Xu, Y., & Fung, P. (2023). A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.

  9. McGee, R. W. (2023). Is chat gpt biased against conservatives? An empirical study. Available at SSRN: https://ssrn.com/abstract=4359405 or https://doi.org/10.2139/ssrn.4359405

  10. McGee, R. W. (2023). Annie Chan: Three Short Stories Written with Chat GPT. Available at SSRN 4359403.

  11. Kitamura, F. C. (2023). ChatGPT is shaping the future of medical writing but still requires human judgment. Radiology, 230171.

  12. Patel, S. B., & Lam, K. (2023). ChatGPT: The future of discharge summaries? The Lancet Digital Health, 5(3), e107–e108.

    Article  Google Scholar 

  13. Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? A preliminary study. arXiv preprint arXiv:2301.08745.

  14. Biswas, S. (2023). ChatGPT and the future of medical writing. Radiology, 223312.

  15. Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023). Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867.

  16. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.

    Article  Google Scholar 

  17. Wang, F. Y., Miao, Q., Li, X., Wang, X., & Lin, Y. (2023). What does chatGPT say: The DAO from algorithmic intelligence to linguistic intelligence. IEEE/CAA Journal of Automatica Sinica, 10(3), 575–579.

    Article  Google Scholar 

  18. Susnjak, T. (2022). ChatGPT: The End of Online Exam Integrity?. arXiv preprint arXiv:2212.09292.

  19. Mijwil, M., Aljanabi, M., & Ali, A. H. (2023). ChatGPT: Exploring the role of cybersecurity in the protection of medical information. Mesopotamian Journal of Cybersecurity, 2023, 18–21.

    Article  Google Scholar 

  20. Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35.

    Article  Google Scholar 

  21. Sallam, M., Salim, N. A., Ala’a, B., Barakat, M., Fayyad, D., Hallit, S., Harappan, H., Hallit, R., & Ala'a, B. (2023). ChatGPT output regarding compulsory vaccination and COVID-19 Vaccine conspiracy: A descriptive study at the outset of a paradigm shift in online search for information. Cureus, 15(2).

  22. Fijačko, N., Gosak, L., Štiglic, G., Picard, C. T., & Douma, M. J. (2023). Can ChatGPT pass the life support exams without entering the American heart association course?. Resuscitation, 185.

  23. Floridi, L. (2023). AI as Agency without Intelligence: On ChatGPT, large language models, and other generative models. Philosophy & Technology, 36(1), 15.

    Article  Google Scholar 

  24. Mbakwe, A. B., Lourentzou, I., Celi, L. A., Mechanic, O. J., & Dagan, A. (2023). ChatGPT passing USMLE shines a spotlight on the flaws of medical education. PLOS Digital Health, 2(2), e0000205.

    Article  Google Scholar 

  25. Khalil, M., & Er, E. (2023). Will ChatGPT get you caught? Rethinking of Plagiarism Detection. arXiv preprint arXiv:2302.04335.

  26. Howard, A., Hope, W., & Gerada, A. (2023). ChatGPT and antimicrobial advice: The end of the consulting infection doctor? The Lancet. Infectious Diseases, 23(4), 405–406. https://doi.org/10.1016/s1473-3099(23)00113-5.

    Article  Google Scholar 

  27. Arif, T. B., Munaf, U., & Ul-Haque, I. (2023). The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Medical Education Online, 28(1), 2181052.

    Article  Google Scholar 

  28. Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in chatgpt: Implications in scientific writing. Cureus, 15(2).

  29. Mathew, A. (2023). Is artificial intelligence a world changer? A case Study of OpenAI’s Chat GPT. Recent Progress in Science and Technology, 5, 35–42.

    Article  Google Scholar 

  30. Antaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023). Evaluating the performance of chatgpt in ophthalmology: An analysis of its successes and shortcomings. medRxiv, 2023–01.

  31. Macdonald, C., Adeloye, D., Sheikh, A., & Rudan, I. (2023). Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. Journal of Global Health, 13.

  32. Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 10776958221149577.

  33. Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023). An analysis of the automatic bug fixing performance of chatgpt. arXiv preprint arXiv:2301.08653.

  34. Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. In: Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning (February 11, 2023).

  35. Pandey, M., Litoriya, R., & Pandey, P. (2018). An ISM approach for modeling the issues and factors of mobile app development. International Journal of Software Engineering and Knowledge Engineering, 28(07), 937–953.

    Article  Google Scholar 

  36. Pandey, M., Litoriya, R., & Pandey, P. (2019). Perception-based classification of mobile apps: A critical review. In: Smart computational strategies: Theoretical and practical aspects, pp 121–133.

  37. Pandey, M., Litoriya, R., & Pandey, P. (2020). Validation of existing software effort estimation techniques in context with mobile software applications. Wireless Personal Communications, 110(4), 1659–1677.

    Article  Google Scholar 

  38. Pandey, M., Litoriya, R., & Pandey, P. (2019). Novel approach for mobile based app development incorporating MAAF. Wireless Personal Communications, 107(4), 1687–1708.

    Article  Google Scholar 

  39. Pandey, M., Litoriya, R., & Pandey, P. (2018). Mobile APP development based on agility function. Ingénierie des Systèmes d'Information, 23(6).

  40. Pandey, M., Litoriya, R., & Pandey, P. (2016, March). Mobile applications in context of big data: A survey. In 2016 Symposium on Colossal Data Analysis and Networking (CDAN) (pp. 1–5). IEEE.

  41. Bustince, H., & Burillo, P. (1996). Vague sets are intuitionistic fuzzy sets. Fuzzy sets and systems, 79(3), 403–405.

    Article  MathSciNet  Google Scholar 

  42. Wei, C. P., Wang, P., & Zhang, Y. Z. (2011). Entropy, similarity measure of interval-valued intuitionistic fuzzy sets and their applications. Information Sciences, 181(19), 4273–4286.

    Article  MathSciNet  Google Scholar 

  43. Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368.

    Article  Google Scholar 

  44. Cornelis, C., Deschrijver, G., & Kerre, E. E. (2004). Implication in intuitionistic fuzzy and interval-valued fuzzy set theory: Construction, classification, application. International Journal of Approximate Reasoning, 35(1), 55–95.

    Article  MathSciNet  Google Scholar 

  45. Opricovic, S., & Tzeng, G. H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635–652.

    Article  MathSciNet  Google Scholar 

  46. Hodgkinson, L. (2000). Is technology masculine? Theorising the absence of women. In University as a Bridge from Technology to Society. IEEE International Symposium on Technology and Society (Cat. No. 00CH37043) (pp. 121–126). IEEE.

  47. de Andrés-Sánchez, J. (2023). A systematic review of the interactions of fuzzy set theory and option pricing. Expert Systems with Applications, 119868.

  48. Yan, H., Yang, Y., Lei, X., Ye, Q., Huang, W., & Gao, C. (2023). Regret Theory and Fuzzy-DEMATEL-based model for construction program manager selection in China. Buildings, 13(4), 838.

    Article  Google Scholar 

  49. Zhang, Z. X., Wang, L., Wang, Y. M., & Martínez, L. (2023). A novel alpha-level sets based fuzzy DEMATEL method considering experts’ hesitant information. Expert Systems with Applications, 213, 118925.

    Article  Google Scholar 

  50. Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). An integrated fuzzy DEMATEL and fuzzy TOPSIS method for analyzing smart manufacturing technologies. Processes, 11(3), 906.

    Article  Google Scholar 

  51. Gohain, B., Chutia, R., & Dutta, P. (2023). A distance measure for optimistic viewpoint of the information in interval-valued intuitionistic fuzzy sets and its applications. Engineering Applications of Artificial Intelligence, 119, 105747.

    Article  Google Scholar 

  52. Liu, X., Sun, Y., Garg, H., & Zhang, S. (2023). Analysis of distance measures in intuitionistic fuzzy set theory: A line integral perspective. Expert Systems with Applications, 226, 120221.

    Article  Google Scholar 

  53. Mahmood, T., & Ali, Z. (2023). Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for managing complex intuitionistic fuzzy sets. Computational and Applied Mathematics, 42(2), 87.

    Article  MathSciNet  Google Scholar 

  54. Li, N., Guo, C., & Liu, Y. (2023). Analysis of factors influencing the efficiency of agricultural cold chain logistics enterprises based on intuitionistic Fuzzy-DEMATEL Method. (Preprint) https://doi.org/10.31223/X5M37P

  55. Ortega-Martín, M., García-Sierra, Ó., Ardoiz, A., Álvarez, J., Armenteros, J. C., & Alonso, A. (2023). Linguistic ambiguity analysis in ChatGPT. arXiv preprint arXiv:2302.06426.

  56. Ventayen, R. J. M. (2023). OpenAI ChatGPT generated results: Similarity index of artificial intelligence-based contents. Available at SSRN 4332664.

  57. Hacker, P., Engel, A., & Mauer, M. (2023). Regulating ChatGPT and other large generative AI models. arXiv preprint arXiv:2302.02337.

  58. Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). " I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. arXiv preprint arXiv:2212.05856.

  59. Zarifhonarvar, A. (2023). Economics of ChatGPT: A labor market view on the occupational impact of artificial intelligence. Available at SSRN 4350925.

  60. Biswas, S. S. (2023). Potential use of Chat GPT in global warming. Annals of Biomedical Engineering, pp 1–2.

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. SR University, Warangal, Telangana, India

    Mamta Pandey

  2. Medi-Caps University, Indore, India

    Ratnesh Litoriya

  3. Jaypee University of Engineering and Technology, Guna, India

    Prateek Pandey

Authors
  1. Mamta Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ratnesh Litoriya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prateek Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ratnesh Litoriya.

Ethics declarations

Conflict of interests

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, M., Litoriya, R. & Pandey, P. Indicators of AI in Automation: An Evaluation Using Intuitionistic Fuzzy DEMATEL Method with Special Reference to Chat GPT. Wireless Pers Commun 134, 445–465 (2024). https://doi.org/10.1007/s11277-024-10917-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-10917-7

Keywords

Search

Navigation