Transformation of Verbal Descriptions of Process Flows into Business Process Modelling and Notation Models Using Multimodal Artificial Intelligence: Application in Justice

Silvia Alayón^1,*, Carlos Martín¹, Jesús Torres¹, Manuel Bacallado¹, Rosa Aguilar¹, Guzmán Savirón²
1 Department of Computer and Systems Engineering, University of La Laguna, San Cristóbal de La Laguna, 38200, Spain
2 Provincial Court of Las Palmas de Gran Canaria, Section 4 (Commercial), Las Palmas de Gran Canaria, 35016, Spain
* Corresponding Author: Silvia Alayón. Email: email
(This article belongs to the Special Issue: Emerging Frontiers and Disruptive Technologies in Computer Science Engineering: Advancements in AI, Machine Learning, and Large Language Models to Shape Intelligent Systems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.073488

Received 19 September 2025; Accepted 04 December 2025; Published online 04 February 2026

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Abstract

Business Process Modelling (BPM) is essential for analyzing, improving, and automating the flow of information within organizations, but traditional approaches based on manual interpretation are slow, error-prone, and require a high level of expertise. This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation (BPMN) diagrams solely from verbal descriptions of the processes to be modeled, utilizing Large Language Models (LLMs) and multimodal Artificial Intelligence (AI). Experimental results, based on video recordings of process explanations provided by an expert from an organization (in this case, the Commercial Courts of a public justice administration), demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams, leading to significant improvements in the speed, accuracy, and accessibility of process modeling. This research makes a substantial contribution to the field of business process modeling, as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material (text and video), combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.