Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?
Abstract
AI-driven chatbots such as ChatGPT have caused a tremendous hype lately. For BPM applications, several applications for AI-driven chatbots have been identified to be promising to generate business value, including explanation of process mining outcomes and preparation of input data. However, a systematic analysis of chatbots for their support of conversational process modeling as a process-oriented capability is missing. This work aims at closing this gap by providing a systematic analysis of existing chatbots. Application scenarios are identified along the process life cycle. Then a systematic literature review on conversational process modeling is performed, resulting in a taxonomy of application scenarios for conversational process modeling, including paraphrasing and improvement of process descriptions. In addition, this work suggests and applies an evaluation method for the output of AI-driven chatbots with respect to completeness and correctness of the process models. This method consists of a set of KPIs on a test set, a set of prompts for task and control flow extraction, as well as a survey with users. Based on the literature and the evaluation, recommendations for the usage (practical implications) and further development (research directions) of conversational process modeling are derived.
Cite
@article{arxiv.2304.11065,
title = {Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?},
author = {Nataliia Klievtsova and Janik-Vasily Benzin and Timotheus Kampik and Juergen Mangler and Stefanie Rinderle-Ma},
journal= {arXiv preprint arXiv:2304.11065},
year = {2024}
}