Artificial intelligence (ai): learning formats in project manager - a systematic literature review

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Published: 2025-05-14

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Original articles

Abstract

Artificial Intelligence (AI) and Deep Learning models are currently a field of computing and informatics that allows many applications and innovations in research. In this sense, for the area of project management, (multidisciplinary field in many other areas), it is impossible that the impact of Artificial Intelligence does not influence their areas of expertise. Therefore, the objective of this study is to analyze the use of artificial intelligence (AI) and its deep learning models in project management and how it can contribute to the efficiency of its processes. Regarding the study method, a systematic literature review was performed following the Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses (PRISMA) model. In the process of literature cleaning, 58 final studies were obtained, of which 32 were chosen as primary sources and 26 as secondary sources. Therefore, three phases were used: phase I=initial, phase II=debugging and phase III=compilation, to select articles from 2,160 documents found among the SCOPUS and SCISPACE journal databases.


 


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Keywords:
Artificial Intelligence, AI, Machine Learning, Neural Networks, Deep Learning, Natural Language Processing, NLP, Project Manager, Systematic Literature Reviews

Article Details

Author Biography

Gali Monpue, Instituto Tecnológico de Santo Domingo (INTEC), Dominican Republic

Technical Coordinator at the Publications Unit, Instituto Tecnológico de Santo Domingo (INTEC), Dominican Republic, email: gali.monpue@intec.edu.do, Orcid: https://orcid.org/0000-0002-3337-8179

How to Cite

Monpue, G. (2025). Artificial intelligence (ai): learning formats in project manager - a systematic literature review. Neuropolis Science Journal, 3(1), 52-80. https://doi.org/10.64029/nsj.2025.v3i1.20

References

Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827. https://doi.org/10.1016/j.jobe.2020.101827

Auth, G., JokischPavel, O., & Dürk, C. (2019). Revisiting automated project management in the digital age – a survey of AI approaches. Online Journal of Applied Knowledge Management (OJAKM), 7(1), 27–39. https://doi.org/10.36965/OJAKM.2019.7(1)27-39

Behrooz, H., Lipizzi, C., Korfiatis, G., Ilbeigi, M., Powell, M., & Nouri, M. (2023). Towards Automating the Identification of Sustainable Projects Seeking Financial Support: An AI-Powered Approach. Sustainability, 15(12), 9701. https://doi.org/10.3390/su15129701

Belharet, A., Bharathan, U., Dzingina, B., Madhavan, N., Mathur, C., Toti, Y.-D. B., Markowski, K., & Babbar, D. (2020). Report on the Impact of Artificial Intelligence on Project Management. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3660689

Bokrantz, J., Subramaniyan, M., & Skoogh, A. (2023). Realising the promises of artificial intelligence in manufacturing by enhancing CRISP-DM. Production Planning & Control, 1–21. https://doi.org/10.1080/09537287.2023.2234882

Brennan, H. L., & Kirby, S. D. (2022). Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea. Journal of Otolaryngology - Head & Neck Surgery, 51(1), 16. https://doi.org/10.1186/s40463-022-00566-w

Čančer, V., Tominc, P., & Rožman, M. (2023). Multi-Criteria Measurement of AI Support to Project Management. IEEE Access, 11, 142816–142828. https://doi.org/10.1109/ACCESS.2023.3342276

Červený, L., Sloup, R., Červená, T., Riedl, M., & Palátová, P. (2022). Industry 4.0 as an Opportunity and Challenge for the Furniture Industry—A Case Study. Sustainability, 14(20), 13325. https://doi.org/10.3390/su142013325

Chang, Y., & Liang, Y. (2023). Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon. Sustainability, 15(7), 5765. https://doi.org/10.3390/su15075765

Ding, C., Huang, X., & Lin, Y. (2023). Optimization and application of artificial intelligence in robotic automated distribution network overhead line engineering. EAI Endorsed Transactions on Energy Web, 10. https://doi.org/10.4108/ew.3718

Dobos, O., & Csiszarik-Kocsir, A. (2022). The Role of Project Management in Cyber Warfare with the Support of Artificial Intelligence. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 17, 26–37. https://doi.org/10.55549/epstem.1175898

Duarte, J., Li, H., Roy, A., Zhu, R., Huerta, E. A., Diaz, D., Harris, P., Kansal, R., Katz, D. S., Kavoori, I. H., Kindratenko, V. V., Mokhtar, F., Neubauer, M. S., Eon Park, S., Quinnan, M., Rusack, R., & Zhao, Z. (2023). FAIR AI models in high energy physics. Machine Learning: Science and Technology, 4(4), 045062. https://doi.org/10.1088/2632-2153/ad12e3

El Khatib, M., & Al Falasi, A. (2021). Effects of Artificial Intelligence on Decision Making in Project Management. American Journal of Industrial and Business Management, 11(03), 251–260. https://doi.org/10.4236/ajibm.2021.113016

Engel, C., Ebel, P., & Van Giffen, B. (2021). Empirically Exploring the Cause-Effect Relationships of AI Characteristics, Project Management Challenges, and Organizational Change. In F. Ahlemann, R. Schütte, & S. Stieglitz (Eds.), Innovation Through Information Systems (Vol. 47, pp. 166–181). Springer International Publishing. https://doi.org/10.1007/978-3-030-86797-3_12

Esztergár-Kiss, D. (2023). Transportation Research Challenges Based on the Analysis of EU Projects. Promet - Traffic&Transportation, 35(4), 446–461. https://doi.org/10.7307/ptt.v35i4.181

Fahimullah, M., Faheem, Y., & Ahmad, N. (2019). Collaboration Formation and Profit Sharing Between Software Development Firms: A Shapley Value Based Cooperative Game. IEEE Access, 7, 42859–42873. https://doi.org/10.1109/ACCESS.2019.2908459

Havstorm, T. E., & Karlsson, F. (2023). Software developers reasoning behind adoption and use of software development methods – a systematic literature review. International Journal of Information Systems and Project Management, 11(2), 47–78. https://doi.org/10.12821/ijispm110203

Herremans, D. (2021). aiSTROM–A Roadmap for Developing a Successful AI Strategy. IEEE Access, 9, 155826–155838. https://doi.org/10.1109/ACCESS.2021.3127548

Holzmann, V., & Lechiara, M. (2022). Artificial Intelligence in Construction Projects: An Explorative Study of Professionals’ Expectations. European Journal of Business and Management Research, 7(3), 151–162. https://doi.org/10.24018/ejbmr.2022.7.3.1432

Khatun, M. T., Hiekata, K., Takahashi, Y., & Okada, I. (2023). Design and management of software development projects under rework uncertainty: A study using system dynamics. Journal of Decision Systems, 32(2), 265–288. https://doi.org/10.1080/12460125.2021.2023257

Khodabakhshian, A., Puolitaival, T., & Kestle, L. (2023). Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis. Buildings, 13(5), 1312. https://doi.org/10.3390/buildings13051312

Lishner, I., & Shtub, A. (2022). Using an Artificial Neural Network for Improving the Prediction of Project Duration. Mathematics, 10(22), 4189. https://doi.org/10.3390/math10224189

Lung, L.-W., & Wang, Y.-R. (2023). Applying Deep Learning and Single Shot Detection in Construction Site Image Recognition. Buildings, 13(4), 1074. https://doi.org/10.3390/buildings13041074

Mahmood, A., Al Marzooqi, A., El Khatib, M., & AlAmeemi, H. (2023). How Artificial Intelligence can leverage Project Management Information system (PMIS) and data driven decision making in project management. International Journal of Business Analytics and Security (IJBAS), 3(1), 180–191. https://doi.org/10.54489/ijbas.v3i1.215

Matos, J. F., Piedade, J., Freitas, A., Pedro, N., Dorotea, N., Pedro, A., & Galego, C. (2023). Teaching and Learning Research Methodologies in Education: A Systematic Literature Review. Education Sciences, 13(2), 173. https://doi.org/10.3390/educsci13020173

Mishra, A., Tripathi, A., & Khazanchi, D. (2022). A Proposal for Research on the Application of AI/ML in ITPM: Intelligent Project Management. International Journal of Information Technology Project Management, 14(1), 1–9. https://doi.org/10.4018/IJITPM.315290

Nawaz, N. (2020). Exploring Artificial Intelligence Applications In Human Resource Management. 23(5).

Noteboom, C., Ofori, M., & Shen, Z. (2023). The Applications of Artificial Intelligence in Managing Project Processes and Targets: A Systematic Analysis. Journal of International Technology and Information Management, 31(3), 77–113. https://doi.org/10.58729/1941-6679.1558

Oliveira, B. A. S., De Faria Neto, A. P., Fernandino, R. M. A., Carvalho, R. F., Fernandes, A. L., & Guimaraes, F. G. (2021). Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning. IEEE Access, 9, 19195–19207. https://doi.org/10.1109/ACCESS.2021.3054468

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Alonso-Fernández, S. (2021). Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790–799. https://doi.org/10.1016/j.recesp.2021.06.016

Prifti, V. (2022). Optimizing Project Management using Artificial Intelligence. European Journal of Formal Sciences and Engineering, 5(1), 30–38. https://doi.org/10.26417/667hri67

Sahadevan, S. (2023). Project Management in the Era of Artificial Intelligence. European Journal of Theoretical and Applied Sciences, 1(3), 349–359. https://doi.org/10.59324/ejtas.2023.1(3).35

Santillan Rojas, J. J., Cabezas Suazo, N. D., Chamorro Monago, J. J., & Aquino Fernandez, A. N. (2023). Artificial intelligence for the management of water projects and the management of water resources: A bibliographical analysis. Journal of Project Management, 8(3), 191–198. https://doi.org/10.5267/j.jpm.2023.2.002

Sarmento Dos Santos-Neto, J. B., & Costa, A. P. C. S. (2023). A Multi-Criteria Decision-Making Model for Selecting a Maturity Model: International Journal of Decision Support System Technology, 15(1), 1–15. https://doi.org/10.4018/IJDSST.319305

Sousa, A. O., Veloso, D. T., Gonçalves, H. M., Faria, J. P., Mendes-Moreira, J., Graça, R., Gomes, D., Castro, R. N., & Henriques, P. C. (2023). Applying Machine Learning to Estimate the Effort and Duration of Individual Tasks in Software Projects. IEEE Access, 11, 89933–89946. https://doi.org/10.1109/ACCESS.2023.3307310

Strang, K. D., & Vajjhala, N. R. (2023). Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods: International Journal of Information Technology Project Management, 14(1), 1–24. https://doi.org/10.4018/IJITPM.317221

Taboada, I., Daneshpajouh, A., Toledo, N., & De Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences, 13(8), 5014. https://doi.org/10.3390/app13085014

Taherdoost, H., & Madanchian, M. (2023). Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation. Computers, 12(4), 72. https://doi.org/10.3390/computers12040072

Tao, F., Pi, Y., Deng, M., Tang, Y., & Yuan, C. (2023). Research on Intelligent Grading Evaluation of Water Conservancy Project Safety Risks Based on Deep Learning. Water, 15(8), 1607. https://doi.org/10.3390/w15081607

Tomczak, M., & Jaśkowski, P. (2022). SCHEDULING REPETITIVE CONSTRUCTION PROJECTS: STRUCTURED LITERATURE REVIEW. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 28(6), 422–442. https://doi.org/10.3846/jcem.2022.16943

Tominc, P., Oreški, D., Čančer, V., & Rožman, M. (2024). Statistically Significant Differences in AI Support Levels for Project Management between SMEs and Large Enterprises. AI, 5(1), 136–157. https://doi.org/10.3390/ai5010008

Tsaih, R.-H., Chang, H.-L., Hsu, C.-C., & Yen, D. C. (2023). The AI Tech-Stack Model. Communications of the ACM, 66(3), 69–77. https://doi.org/10.1145/3568026

Vărzaru, A. A. (2022). An Empirical Framework for Assessing the Digital Technologies Users’ Acceptance in Project Management. Electronics, 11(23), 3872. https://doi.org/10.3390/electronics11233872

Vial, G., Cameron, A., Giannelia, T., & Jiang, J. (2023). Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33(3), 669–691. https://doi.org/10.1111/isj.12420

Wang, J. (2022). A Business Management Resource-Scheduling Method based on Deep Learning Algorithm. Mathematical Problems in Engineering, 2022, 1–9. https://doi.org/10.1155/2022/1122024

Wauters, M., & Vanhoucke, M. (2017). A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence. European Journal of Operational Research, 259(3), 1097–1111. https://doi.org/10.1016/j.ejor.2016.11.018

Witte, F. (2022). Strategy, Planning and Organization of Test Processes: Basis for Successful Project Execution in Software Testing. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-36981-1

Younis, M. S., & . E. (2022). THE BENEFITS OF ARTIFICIAL INTELLIGENCE IN CONSTRUCTION PROJECTS. Acta Informatica Malaysia, 6(2), 47–51. https://doi.org/10.26480/aim.02.2022.47.51

Yu, K., Froese, T., & Grobler, F. (2000). A development framework for data models for computer-integrated facilities management. Automation in Construction, 9(2), 145–167. https://doi.org/10.1016/S0926-5805(99)00002-3

Zeiner-Gundersen, D. H., & Winner, V. (n.d.). Intelligence (AI) Driven Algorithms When Addressing Project Costs and Risks.

Проскурін, В. М., Морозов, В. В., & Шелест, Т. М. (2019). THE MODEL OF IT PROJECT MANAGEMENT SYSTEM BASED ON MACHINE LEARNING. Bulletin of NTU “KhPI”. Series: Strategic Management, Portfolio, Program and Project Management, 0(1(1326)), 42–50. https://doi.org/10.20998/2413-3000.2019.1326.7

Selección de los años para esta investigación:

2023

(Taherdoost & Madanchian, 2023)

(Santillan Rojas et al., 2023)

(Khodabakhshian et al., 2023)

(Mahmood et al., 2023)

(Vial et al., 2023)

(Čančer et al., 2023)

(Behrooz et al., 2023)

(Esztergár-Kiss, 2023)

(Sarmento Dos Santos-Neto & Costa, 2023)

(Lung & Wang, 2023)

(Khatun et al., 2023)

(Duarte et al., 2023)

(Červený et al., 2022)

(Chang & Liang, 2023)

(Bokrantz et al., 2023)

(Tao et al., 2023)

(Tsaih et al., 2023)

2022

Wang, 2022)

(Vărzaru, 2022)

(Holzmann & Lechiara, 2022)

(Younis & ., 2022)

(Dobos & Csiszarik-Kocsir, 2022)

(Lishner & Shtub, 2022)

(Brennan & Kirby, 2022)

(Červený et al., 2022)

(Witte, 2022)

2021

(Engel et al., 2021)

(Herremans, 2021)

(Oliveira et al., 2021)

2020

(Akinosho et al., 2020)

(Belharet et al., 2020)

(Nawaz, 2020)

2019

(Fahimullah et al., 2019)

(Проскурін et al., 2019)