The DOI matches its Crossref record.
Advancing industry 4.0, 5.0, and society 5.0 through generative artificial intelligence like ChatGPT
Open accessAbstract
The Industry 4.0, Industry 5.0, and Society 5.0 are increasingly advanced by generative artificial intelligence (AI) typified by models like ChatGPT. As the researchers have placed an emphasis on further upgrading industrial sector, it is Industry 4.0 - using sensors, Internet of Things (IoT), and AI to automate and optimize production, as well as cyber-physical systems to monitor physical processes - that best fits the current needs of industries. Generative AI can further enhance these systems by enabling real-time data analysis, predictive maintenance, and automated decision-making, leading to improved efficiency, reduced downtime and fostering more innovation in manufacturing. Generative AI plays a role in helping the industry transition to Industry 5.0 and the reinforcement among human-centric applications with shared workflow by humans and machines. It helps in customization of user experiences, intelligent decision support systems, and human-robot cooperation. In the paradigm of Society 5.0, that is a next stage super-smart society where digital and physical spaces are fused into one guaranteed reality, generative AI is the other half of creating a viable, healthy and comprehensive neighbourhood. Technology, powered by Natural Language Processing (NLP) and generative AI like ChatGPT, facilitates the advancement of human-cantered and sustainable practices that can help create a more efficient societies and industries.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Generative AI is a strong development tool in green innovations, allowing businesses to design sustainable products and optimize processes concerning operative consumption levels.
“Generative AI is coming out as a strong development tool in green innovations, allowing businesses to design sustainable products and optimize processes concerning operative consumption levels.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [55]
Generative AI product design can reduce raw material usage, lower production costs, and reduce environmental impacts.
“This can lead to a strong reduction in the usage of raw materials, lowering the costs of production, and reducing environmental impacts due to the creation of new products55,56.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [55]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 66
- Abas, M. A., Arumugam, S. E., Yunus, M. M., & Rafiq, K. R. M. (2023). ChatGPT and personalized learning: Opportunities and challenges in higher education. International Journal of Academic Research in Business and Social Sciences, 13(12).
- Abdullah, M., Madain, A., & Jararweh, Y. (2022, November). ChatGPT: Fundamentals, applications and social impacts. In 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 1-8). Ieee.
- Al Fouri, A., & Sakher, S. (2023). Artificial intelligence on the smart city. Remittances Review, 8(4).
- Aladağ, H. (2023). Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects. Sustainability, 15(22), 16071.
- Alawida, M., Mejri, S., Mehmood, A., Chikhaoui, B., & Isaac Abiodun, O. (2023). A comprehensive study of ChatGPT: advancements, limitations, and ethical considerations in natural language processing and cybersecurity. Information, 14(8), 462.
- Al-Hawawreh, M., Aljuhani, A., & Jararweh, Y. (2023). Chatgpt for cybersecurity: practical applications, challenges, and future directions. Cluster Computing, 26(6), 3421-3436.
- Alshami, A., Elsayed, M., Ali, E., Eltoukhy, A. E., & Zayed, T. (2023). Harnessing the power of ChatGPT for automating systematic review process: Methodology, case study, limitations, and future directions. Systems, 11(7), 351.
- Cao, X. (2023). A new era of intelligent interaction: Opportunities and challenges brought by ChatGPT. Geographical Research Bulletin, 2, 162-165.
- Chen, X. (2023). Embedded Way of Responsible Innovation in ChatGPT. Traektoriâ Nauki, 9(6), 7001-7006.
- Demir, M., & Demir, Ş. Ş. (2023). Is ChatGPT the right technology for service individualization and value co-creation? evidence from the travel industry. Journal of Travel & Tourism Marketing, 40(5), 383-398.
- Filippi, S. (2023). Measuring the impact of ChatGPT on fostering concept generation in innovative product design. Electronics, 12(16), 3535.
- Frederico, G. F. (2023). ChatGPT in supply chains: initial evidence of applications and potential research agenda. Logistics, 7(2), 26.
- George, A. S., & George, A. H. (2023). A review of ChatGPT AI's impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9-23.
- George, A. S., George, A. H., & Martin, A. G. (2023). ChatGPT and the future of work: a comprehensive analysis of AI'S impact on jobs and employment. Partners Universal International Innovation Journal, 1(3), 154-186.
- George, A. S., George, A. H., & Martin, A. G. (2023). The environmental impact of ai: A case study of water consumption by chat gpt. Partners Universal International Innovation Journal, 1(2), 97-104.
- Gupta, M., Akiri, C., Aryal, K., Parker, E., & Praharaj, L. (2023). From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy. IEEE Access.
- Harahap, M. A. K., Junianto, P., Astutik, W. S., Risdwiyanto, A., & Ausat, A. M. A. (2023). Use of ChatGPT in Building Personalisation in Business Services. Jurnal Minfo Polgan, 12(1), 1212-1219.
- Haver, H. L., Bahl, M., Doo, F. X., Kamel, P. I., Parekh, V. S., Jeudy, J., & Yi, P. H. (2024). Evaluation of multimodal ChatGPT (GPT-4V) in describing mammography image features. Canadian Association of Radiologists Journal, 08465371241247043.
- Huang, H., Zheng, O., Wang, D., Yin, J., Wang, Z., Ding, S., ... & Shi, B. (2023). ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. International Journal of Oral Science, 15(1), 29.
- Huang, S., Wang, B., Li, X., Zheng, P., Mourtzis, D., & Wang, L. (2022). Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution. Journal of manufacturing systems, 64, 424-428.
- Jarco, D., & Sulkowski, L. (2023, June). Is ChatGPT better at business consulting than an experienced human analyst? An experimental comparison of solutions to a strategic business problem. In Forum Scientiae Oeconomia (Vol. 11, No. 2, pp. 87-109).
- Javaid, M., Haleem, A., & Singh, R. P. (2023). A study on ChatGPT for Industry 4.0: Background, potentials, challenges, and eventualities. Journal of Economy and Technology, 1, 127-143.
- Javaid, M., Haleem, A., & Singh, R. P. (2023). ChatGPT for healthcare services: An emerging stage for an innovative perspective. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(1), 100105.
- Jusman, I. A., Ausat, A. M. A., & Sumarna, A. (2023). Application of ChatGPT in Business Management and Strategic Decision Making. Jurnal Minfo Polgan, 12(2), 1688-1697.
- Khowaja, S. A., Khuwaja, P., Dev, K., Wang, W., & Nkenyereye, L. (2024). Chatgpt needs spade (sustainability, privacy, digital divide, and ethics) evaluation: A review. Cognitive Computation, 1-23.
- Kiangala, K. S., & Wang, Z. (2024). An experimental hybrid customized AI and generative AI chatbot human machine interface to improve a factory troubleshooting downtime in the context of Industry 5.0. The International Journal of Advanced Manufacturing Technology, 132(5), 2715-2733.
- Kmiecik, M. (2023). ChatGPT in third-party logistics–The game-changer or a step into the unknown?. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100174.
- Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., ... & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279-295.
- Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410.
- Megahed, F. M., Chen, Y. J., Ferris, J. A., Knoth, S., & Jones-Farmer, L. A. (2024). How generative AI models such as ChatGPT can be (mis) used in SPC practice, education, and research? An exploratory study. Quality Engineering, 36(2), 287-315.
- Muftić, F., Kadunić, M., Mušinbegović, A., & Abd Almisreb, A. (2023). Exploring Medical breakthroughs: a systematic review of ChatGPT Applications in Healthcare. Southeast Europe Journal of Soft Computing, 12(1), 13-41.
- Nyqvist, R., Peltokorpi, A., & Seppänen, O. (2024). Can ChatGPT exceed humans in construction project risk management?. Engineering, Construction and Architectural Management, 31(13), 223-243.
- Özpolat, Z., Yıldırım, Ö., & Karabatak, M. (2023). Artificial Intelligence-based tools in software development processes: Application of ChatGPT. European Journal of Technique (EJT), 13(2), 229-240.
- Pan, C., Banerjee, J. S., De, D., Sarigiannidis, P., Chakraborty, A., & Bhattacharyya, S. (2023, February). ChatGPT: A OpenAI platform for society 5.0. In Doctoral Symposium on Human Centered Computing (pp. 384-397). Singapore: Springer Nature Singapore.
- Paramesha, M., Rane, N. L., & Rane, J. (2024a). Artificial Intelligence, Machine Learning, Deep Learning, and Blockchain in Financial and Banking Services: A Comprehensive Review. Partners Universal Multidisciplinary Research Journal, 1(2), 51-67.
- Paramesha, M., Rane, N., & Rane, J. (2024b). Trustworthy Artificial Intelligence: Enhancing Trustworthiness Through Explainable AI (XAI). Available at SSRN 4880090.
- Paramesha, M., Rane, N., & Rane, J. (2024c). Artificial intelligence in transportation: applications, technologies, challenges, and ethical considerations. Available at SSRN 4869714.
- Paramesha, M., Rane, N., & Rane, J. (2024d). Generative artificial intelligence such as ChatGPT in transportation system: A comprehensive review. Available at SSRN 4869724.
- Paramesha, M., Rane, N., & Rane, J. (2024e). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Available at SSRN 4855856.
- Prieto, S. A., Mengiste, E. T., & García de Soto, B. (2023). Investigating the use of ChatGPT for the scheduling of construction projects. Buildings, 13(4), 857.
- Raj, R., Singh, A., Kumar, V., & Verma, P. (2023). Analyzing the potential benefits and use cases of ChatGPT as a tool for improving the efficiency and effectiveness of business operations. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(3), 100140.
- Raman, R., Lathabai, H. H., Mandal, S., Das, P., Kaur, T., & Nedungadi, P. (2024). ChatGPT: Literate or intelligent about UN sustainable development goals?. Plos one, 19(4), e0297521.
- Rane, N. (2023d). Role and challenges of ChatGPT and similar generative artificial intelligence in human resource management. Available at SSRN 4603230.
- Rane, N. (2023e). Contribution of ChatGPT and other generative artificial intelligence (AI) in renewable and sustainable energy. Available at SSRN 4597674.
- Rao, H. (2023). Ethical and legal considerations behind the prevalence of ChatGPT: risks and regulations. Frontiers in Computing and Intelligent Systems, 4(1), 23-29.
- Rathor, K. (2023). Impact of using Artificial Intelligence-Based Chatgpt Technology for Achieving Sustainable Supply Chain Management Practices in Selected Industries. International Journal of Computer Trends and Technology, 71(3), 34-40.
- Rathore, B. (2023). Future of textile: Sustainable manufacturing & prediction via chatgpt. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 12(1), 52-62.
- Sherif, A., Salloum, S. A., & Shaalan, K. (2024). Systematic Review for Knowledge Management in Industry 4.0 and ChatGPT Applicability as a Tool. Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom, 301-313.
- Singh, G. (2023). Leveraging chatgpt for real-time decision-making in autonomous systems. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 12(2), 101-106.
- Skandali, D., Magoutas, A., & Tsourvakas, G. (2023). Artificial Intelligent Applications in Enabled Banking Services: The Next Frontier of Customer Engagement in the Era of ChatGPT. Theoretical Economics Letters, 13(5), 1203-1223.
- Sonkor, M. S., & García de Soto, B. (2024). Using ChatGPT in construction projects: unveiling its cybersecurity risks through a bibliometric analysis. International Journal of Construction Management, 1-9.
- Stahl, B. C., & Eke, D. (2024). The ethics of ChatGPT–Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74, 102700.
- Tabone, W., & de Winter, J. (2023). Using ChatGPT for human–computer interaction research: a primer. Royal Society Open Science, 10(9), 231053.
- Tarabah, N. E. H., & Amin, M. E. S. (2024). ChatGPT and Its Role in Revolutionizing Digital Marketing and Enhancing Customer Engagement. In Leveraging ChatGPT and Artificial Intelligence for Effective Customer Engagement (pp. 69-92). IGI Global.
- Trent, C. (2023). ChatGPT and Current Events in the Economics Classroom. Business Education Innovation Journal, 15(1).
- Uddin, S. J., Albert, A., Ovid, A., & Alsharef, A. (2023). Leveraging ChatGPT to aid construction hazard recognition and support safety education and training. Sustainability, 15(9), 7121.
- Vemprala, S. H., Bonatti, R., Bucker, A., & Kapoor, A. (2024). Chatgpt for robotics: Design principles and model abilities. IEEE Access.
- Wake, N., Kanehira, A., Sasabuchi, K., Takamatsu, J., & Ikeuchi, K. (2023). Chatgpt empowered long-step robot control in various environments: A case application. IEEE Access.
- Wakunuma, K., & Eke, D. (2024). Africa, ChatGPT, and Generative AI Systems: Ethical Benefits, Concerns, and the Need for Governance. Philosophies, 9(3), 80.
- Wang, D. Q., Feng, L. Y., Ye, J. G., Zou, J. G., & Zheng, Y. F. (2023). Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare. MedComm–Future Medicine, 2(2), e43.
- Wang, F. Y., Yang, J., Wang, X., Li, J., & Han, Q. L. (2023). Chat with ChatGPT on industry 5.0: Learning and decision-making for intelligent industries. IEEE/CAA Journal of Automatica Sinica, 10(4), 831-834.
- Whalen, J., & Mouza, C. (2023). ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1), 1-23.
- Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
- Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of manufacturing systems, 61, 530-535.
- Yang, L., Wang, Z., Li, Z., Na, J. C., & Yu, J. (2024). An empirical study of Multimodal Entity-Based Sentiment Analysis with ChatGPT: Improving in-context learning via entity-aware contrastive learning. Information Processing & Management, 61(4), 103724.
- Zhang, X., Shah, J., & Han, M. (2023). ChatGPT for fast learning of positive Energy District (PED): A trial testing and comparison with expert discussion results. Buildings, 13(6), 1392.
Cited by
Sorted by citations
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057420 · Cited by 44
Şahin, O., & Karayel, D. (2024) · Journal of Smart Systems Research · doi:10.58769/joinssr.1597110 · Cited by 37
Aljohani, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-09569-9 · Cited by 30
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057412 · Cited by 36
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057434 · Cited by 26
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057432 · Cited by 28
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057404 · Cited by 34
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057414 · Cited by 18
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057408 · Cited by 18
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057400 · Cited by 10
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057410 · Cited by 13
Asuai, C., Atumah, C. T., & Joseph-Brown, A. A. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.140400041 · Cited by 6
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057438 · Cited by 9
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057424 · Cited by 12
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057426 · Cited by 5
Patil, D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5021248 · Cited by 4
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057396 · Cited by 8
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057418 · Cited by 7
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057430 · Cited by 4
Ethical Challenges And Future Implications Of Artificial Intelligence In Society 5.0Preprint
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057398
Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057428
Zhang, Y. (2025) · International Journal of Research Studies in Management · doi:10.5861/ijrsm.2025.25017
(Generative) AI and Machine Learning Solutions: From Data to Decisions
Василева, С. (2026) · Sustainability Organization Business and Economic Research · doi:10.66414/sober.291163
Virmani, N., Singh, R. K., & Gupta, S. (2026) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-026-00501-2
Awwad, B., & Rimawi, B. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83742-264-720261004
Metadata
- Authors
- Rane, N. L., Kaya, Ö., & Rane, J.
- Affiliations
- University of Mumbai (IN)
- Erzurum Technical University (TR)
- Venue
- Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0
- Publisher
- Deep Science Publishing
- Published
- 2024-10-14
- OpenAlex
- W4403656539
- Cited by
- 25
- References
- 0
- Citations by year
- 2026: 3 · 2025: 20 · 2024: 2
- FWCI
- 19.59
- Type
- book chapter
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-sa
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Keywords
- generative AI, ChatGPT, Industry 4.0, Industry 5.0, Society 5.0, Internet of Things, cyber-physical systems, predictive maintenance, human-robot collaboration, human-centered AI, decision support system, manufacturing automation, natural language processing, sustainable society
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review