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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2026
    • vol. 9, nº 6, march 2026
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    • Revista IJIMAI
    • 2026
    • vol. 9, nº 6, march 2026
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    The Application of Large Language Models and Virtual Assistants in Project Management Research: A Review

    Autor: 
    Gil Ruiz, Jesús
    ;
    Zayas-Gallardo, Javier
    ;
    Díaz Rodríguez, Hernán
    Fecha: 
    26/03/2026
    Palabra clave: 
    Business Decisions; Generative AI; LLMs; Virtual Assistants
    Revista / editorial: 
    UNIR
    Citación: 
    J. Gil Ruiz, J. Zayas Gallardo, H. Díaz Rodríguez. The Application of Large Language Models and Virtual Assistants in Project Management Research: A Review, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 9, no. 6, pp. 105-115, 2026, http://doi.org/10.9781/ijimai.2026.2230
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/19150
    DOI: 
    https://doi.org/10.9781/ijimai.2026.2230
    Dirección web: 
    https://www.ijimai.org/index.php/ijimai/article/view/2230
    https://www.ijimai.org/index.php/ijimai/article/view/2230
    Open Access
    Resumen:
    The rapid evolution of generative artificial intelligence (AI) is transforming project management practices by enhancing efficiency, productivity and adaptability in decision-making processes. The integration of large language models (LLMs) into project management research and practice is reviewed, with a particular focus on virtual assistants as decision support tools. State-of-the-art models such as Mistral, Large Language Model Meta AI (LLaMa), Bidirectional Encoder Representations from Transformers (BERT) and T5, are assessed for their potential to automate complex project tasks, extract insights from project datasets, and optimize decision-making across various project management domains and business sectors. Generative AI is shown to surpass traditional project management systems by not only analysing historical project data but also generating new strategies and solutions in real time. Applications include project risk assessment, resource allocation optimisation, stakeholder communication and project performance prediction. The role of fine-tuning and retraining LLMs to adapt them to industry-specific project management challenges is also examined enhancing relevance and performance across diverse business environments.
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