From Smart Workplaces to Human-Centered AI: A Comprehensive Review of Artificial Intelligence and Ergonomics Integration

The increasing integration of artificial intelligence (AI) into workplace systems presents both opportunities and challenges for advancing ergonomics. The literature address how AI can be harmonized with ergonomic principles to enhance workplace design, safety, and human well-being. This gap underscores the need for a systematic synthesis of research on AI-driven ergonomic applications. The study aims to (i) analyze the existing body of research on AI-driven ergonomic applications, (ii) construct a conceptual map capturing the intersections of human, technological, and organizational factors, (iii) identify the contributions of key topic experts, and (iv) synthesize emerging themes that define future directions.
A comprehensive review was conducted using Scopus AI (25 September 2025). The method involved retrieving and analyzing relevant documents through search strings incorporating terms related to AI, ergonomics, workplace design, and integration. Scopus AI tools such as Summary, Expanded Summary, Concept Map, Topic Experts, and Emerging Themes were applied to identify patterns in research productivity, thematic structures, and knowledge gaps.
The findings reveal that AI integration with ergonomics has yielded applications across diverse sectors, including workplace health and safety, smart manufacturing, automotive design, and fashion manufacturing. Consistent themes such as AI in smart manufacturing, human-AI collaboration, and AI in human resource management highlight ongoing advancements, while rising themes such as AI-powered wearable technology, occupational health and safety, and smart building systems indicate new frontiers. Challenges related to ethics, data privacy, workforce readiness, and organizational resistance were also identified.
The study provides both theoretical and practical implications. Theoretically, it expands ergonomic discourse by situating it within human-centered AI frameworks, while practically, it offers insights for organizations seeking to implement AI solutions responsibly. These findings highlight the transformative potential of AI-driven ergonomics while emphasizing the need for ethical, sustainable, and user-centered integration.

Artificial Intelligence (AI), Ergonomics, Human-Centered Design

1. Anacleto Filho, P. C., Colim, A., Jesus, C., Lopes, S. I., & Carneiro, P. (2024). Digital and virtual technologies for work-related biomechanical risk assessment: A scoping review. Safety, 10(3), 79. https://doi.org/10.3390/safety10030079 [Google Scholar] [Crossref]

2. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010 [Google Scholar] [Crossref]

3. Balaji, K. (2025). AI-powered ergonomics: Transforming workplace health and safety. In Cases on AI innovations in occupational health and safety (pp. 51–72). IGI Global. https://doi.org/10.4018/979-8-3693-9301-7.ch003 [Google Scholar] [Crossref]

4. Bisht, R., & Uniyal, A. K. (2024). The future of job satisfaction in a hyper-connected world: AI and IoT perspectives. In Proceedings of the 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA 2024) (pp. 380–384). IEEE. https://doi.org/10.1109/ICECA63461.2024.10801128 [Google Scholar] [Crossref]

5. Bondarenko, T., Ruutmann, T., Kupriyanov, O., Yahupov, V., Marina, R., & Poliakov, M. (2025). Testing platforms augmented with artificial intelligence and educational templates. In Lecture Notes in Networks and Systems (Vol. 1260, pp. 470–477). Springer. https://doi.org/10.1007/978-3-031-85652-5_47 [Google Scholar] [Crossref]

6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

7. Deswal, P., & Arora, N. (2024). Interplay between workplace spirituality and employee wellbeing: The mediating roles of EI and AI. In Practices, challenges, and deterrents in workplace wellbeing: Strategies for building resilient and thriving workplaces (pp. 45–65). IGI Global. https://doi.org/10.4018/979-8-3693-6079-8.ch003 [Google Scholar] [Crossref]

8. Donisi, L., Cesarelli, G., Pisani, N., Ponsiglione, A. M., Ricciardi, C., & Capodaglio, E. (2022). Wearable sensors and artificial intelligence for physical ergonomics: A systematic review of literature. Diagnostics, 12(12), 3048. https://doi.org/10.3390/diagnostics12123048 [Google Scholar] [Crossref]

9. Doran, E., Bommer, S., & Badiru, A. (2022). Integration of human factors, cognitive ergonomics, and artificial intelligence in the human-machine interface for additive manufacturing. International Journal of Mechatronics and Manufacturing Systems, 15(4), 310–330. https://doi.org/10.1504/ijmms.2022.127213 [Google Scholar] [Crossref]

10. Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32. https://doi.org/10.5465/amj.2007.24160888 [Google Scholar] [Crossref]

11. El Hassani, I., Masrour, T., Hajji, T., El Ouardi, F. Z., & Mimoune, N. (2023). Smart ergonomy: Development of an automated METEO assessment based on computer vision. In Lecture Notes in Networks and Systems (Vol. 771, pp. 181–193). Springer. https://doi.org/10.1007/978-3-031-43524-9_13 [Google Scholar] [Crossref]

12. Fiegler-Rudol, J., Lau, K., Mroczek, A., & Kasperczyk, J. (2025). Exploring human–AI dynamics in enhancing workplace health and safety: A narrative review. International Journal of Environmental Research and Public Health, 22(2), 199. https://doi.org/10.3390/ijerph22020199 [Google Scholar] [Crossref]

13. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Advances in psychology (Vol. 52, pp. 139–183). Elsevier. https://doi.org/10.1016/S0166-4115(08)62386-9 [Google Scholar] [Crossref]

14. Leão, C. P., Silva, V., & Costa, S. (2024). Exploring the intersection of ergonomics, design thinking, and AI/ML in design innovation. Applied System Innovation, 7(4), 65. https://doi.org/10.3390/asi7040065 [Google Scholar] [Crossref]

15. Liao, Q. V., Gruen, D., & Miller, S. (2020). Questioning the AI: Informing design practices for explainable AI user experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. https://doi.org/10.1145/3313831.3376590 [Google Scholar] [Crossref]

16. Malek, M. D. A., & Kamil, I. S. M. (2025). Cognitive ergonomics in smart manufacturing: Aligning technology with human capabilities. In Integrating digital innovation and integrated frameworks in manufacturing (pp. 141–170). IGI Global. https://doi.org/10.4018/979-8-3373-1082-4.ch007 [Google Scholar] [Crossref]

17. Pluchino, P., Zordan, F., Santus, V., Zanella, A., Spagnolli, A., Cordibella, S., Sozza, A., Spatharos, A., Grigoletto, A., Borgo, M., Lorenzin, A., & Gamberini, L. (2025). Empowering workers with IoT: Enhancing acceptance and security of resilient smart workplaces. In Lecture Notes in Computer Science (Vol. 15803, pp. 365–381). Springer. https://doi.org/10.1007/978-3-031-92980-9_23 [Google Scholar] [Crossref]

18. Priyanka, M., & Subashini, R. (2024). Does artificial intelligence mediate between ergonomics and the drivers of ergonomics innovations—An empirical evidence. International Research Journal of Multidisciplinary Scope, 5(2), 162–174. https://doi.org/10.47857/irjms.2024.v05i02.0398 [Google Scholar] [Crossref]

19. Puertas, C. A. P., & Galhardi, A. C. (2024). Integrating ergonomic and artificial intelligence in the automotive. SAE Technical Papers. SAE International. https://doi.org/10.4271/2023-36-0042 [Google Scholar] [Crossref]

20. Somaraju, P., Kulkarni, S. S., Duffy, V. G., & Kanade, S. (2024). Artificial intelligence and mobile computing: Role of AI in ergonomics. In Lecture Notes in Computer Science (Vol. 14711, pp. 265–281). Springer. https://doi.org/10.1007/978-3-031-61066-0_16 [Google Scholar] [Crossref]

21. Taslim, W. S., Rosnani, T., & Fauzan, R. (2025). Employee involvement in AI-driven HR decision-making: A systematic review. SA Journal of Human Resource Management, 23, a2856. https://doi.org/10.4102/sajhrm.v23i0.2856 [Google Scholar] [Crossref]

22. Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee wellbeing in the workplace: An empirical study. Journal of Business Research, 199, 115584. https://doi.org/10.1016/j.jbusres.2025.115584 [Google Scholar] [Crossref]

23. Zhang, Y., Joneurairatana, E., & Vongphantuset, J. (2024). An AI-driven decision support framework for ergonomic optimization in fashion manufacturing: Integrating predictive analytics and MCDM techniques. Decision Making: Applications in Management and Engineering, 7(1), 786–802. https://doi.org/10.31181/dmame7120241449 [Google Scholar] [Crossref]

• Impact of Foreign Direct Investment in India
• Issues Involved in Digitalisation Special Reference to Indian Tourism Growth
• Relationship Marketing and Customer Loyalty in the Fast-Moving Consumer Goods (FMCG) Industry in Nairobi County
• Financial Literacy or Financial Inclusion? Which is Which, What is What—To Achieve Uganda’s 10-Fold Economic Growth By 2040
• Harnessing Natural Gas for Economic Transformation: Overcoming the Regulatory and Infrastructural Bottlenecks in Nigeria