A Framework for Model-Driven AI-Assisted Generation of IT Project Management Plan and Scope Documents

This paper presents a model-driven AI-assisted approach for generating IT project management plan and scope documents, aiming to improve efficiency and quality in software development projects.Effective documentation in the early project phase is critical, yet often resource-intensive.The proposed solution consolidates best practices from widely used project management methodologies and standards to create a dynamic, adaptable framework for document generation.The study identifies key components and input data required for generating high-quality plans using model transformations and generative AI.A prototype supporting the solution is developed featuring a local processing engine, integrated with a large language model, a vector database, and an embedded model.

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Publication Details

Journal
Annals of Computer Science and Information Systems
Published
2025-10-15
DOI
https://doi.org/10.15439/2025f8736
Citations
2
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
4.97
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article

A Framework for Model-Driven AI-Assisted Generation of IT Project Management Plan and Scope Documents

Óscar Pastor, Oksana Ņikiforova, Jānis Rihards Blazevičs
2 citations
Annals of Computer Science and Information Systems
Business Process Modeling and Analysis
4.97
article

A Framework for Model-Driven AI-Assisted Generation of IT Project Management Plan and Scope Documents

Óscar Pastor, Oksana Ņikiforova, Jānis Rihards Blazevičs
article en
2 citations

Abstract

This paper presents a model-driven AI-assisted approach for generating IT project management plan and scope documents, aiming to improve efficiency and quality in software development projects.Effective documentation in the early project phase is critical, yet often resource-intensive.The proposed solution consolidates best practices from widely used project management methodologies and standards to create a dynamic, adaptable framework for document generation.The study identifies key components and input data required for generating high-quality plans using model transformations and generative AI.A prototype supporting the solution is developed featuring a local processing engine, integrated with a large language model, a vector database, and an embedded model.

Annals of Computer Science and Information SystemsVol. 43
Riga Technical University (LV), Universitat Politècnica de València (ES)
Openalex Percentile: Top 4%
Business Process Modeling and Analysis
4.97
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