BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI

Björn-Lennart Eger; Daniel Rose; Barbara Dinter

cs.AIAug 27, 2026

BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI

Authors: Björn-Lennart Eger, Daniel Rose, Barbara Dinter

Organizations: Chemnitz University of Technology, Chair of Business Information Systems - Business Process and Information Management, Chemnitz, Germany

Abstract

Conversational AI systems, such as chatbots and virtual assistants, are becoming increasingly important to digital business processes. However, the established Business Process Model and Notation (BPMN) standard faces challenges when representing dynamic, context-sensitive interactions. This paper addresses this methodological and practical research gap by developing a standard-compliant BPMN extension (BPMN4CAI). Using Design Science Research methodology, this paper develops an approach that systematically extends existing BPMN elements and incorporates specialized components. The applicability and relevance of the BPMN4CAI framework are demonstrated and evaluated through a case study. The results show that the BPMN4CAI extension facilitates adaptive decision-making processes, robust context management, and transparent interactions for Conversational AI within business processes.

Business Process Modeling

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Apr 15, 2026J/K move · Enter open · S save

Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends

João Bettencourt, Sérgio Guerreiro

INESC-ID, R. Alves Redol 9, 1000-029 Lisbon, Portugal · Instituto Superior T´ecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

Business Process Modeling

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Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or assisting Business Process Modeling tasks using natural language. Several approaches have been proposed to transform textual process descriptions into BPMN and related workflow models. However, the extent to which these approaches effectively support complex process modeling in organizational settings remains unclear. This article presents a literature review of AI-driven methods for transforming natural language into BPMN process models, with a particular focus on the role of LLMs. Following a structured review strategy, relevant studies were identified and analyzed to classify existing approaches, examine how LLMs are integrated into text-to-model pipelines, and investigate the evaluation practices used to assess generated models. The analysis reveals a clear shift from rule-based and traditional NLP pipelines toward LLM-based architectures that rely on prompt engineering, intermediate representations, and iterative refinement mechanisms. While these approaches significantly expand the capabilities of automated process model generation, the literature also exposes persistent challenges related to semantic correctness, evaluation fragmentation, reproducibility, and limited validation in real-world organizational contexts. Based on these findings, this review identifies key research gaps and discusses promising directions for future research, including the integration of contextual knowledge through Retrieval-Augmented Generation (RAG), its integration with LLMs, the development of interactive modeling architectures, and the need for more comprehensive and standardized evaluation frameworks.

Abstract

Business Process Modeling

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