Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?
Nataliia Klievtsova, Janik-Vasily Benzin, Timotheus Kampik, Juergen Mangler, Stefanie Rinderle-Ma
TL;DR
This work investigates whether generative AI chatbots can empower domain experts to create and redesign process models. It introduces Conversational Process Modelling (ConverMod) and uses design science research to develop a taxonomy, a test set, KPIs, prompts, and an evaluation pipeline combining quantitative, qualitative, and survey methods. The study finds that GPT-4, especially with MER-based textual representations and paraphrase-enhanced inputs, achieves high completeness and reasonable correctness, but performance varies with description clarity and task granularity, necessitating expert validation. Practically, ConverMod can accelerate information gathering and initial modelling, while deeper analytical tasks should be augmented with traditional BPM tools; future work should fuse language models with knowledge-based systems and refine evaluation frameworks.
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.