# Generating Process Models by Interacting with Chatbots--A Literature Review
## 6 Discussion
### Experiment
#### 6.2.3 Study Results  
The experiment yielded promising results, demonstrating that chatbots can indeed generate basic process models from natural language text. The prototypes were able to create initial models, modify them through textual descriptions, and even handle simple user queries regarding the models. These results show that, in principle, current chatbot technology has the potential to support this type of interaction, sometimes with surprisingly good outcomes. However, the study also revealed several limitations. More complex scenarios, such as multi-party processes involving multiple pools or message flows, were not adequately handled by the prototypes. Additionally, while the basic functionality works, further research is needed to assess the quality of the models and improve the handling of more sophisticated process features. To illustrate the progress made and provide a clearer picture of what is currently feasible with chatbot technology, Figures 6 and 7 present screenshots of the prototype.  
What works well: Chatbots can generate and modify simple process models, support interactive refinement, and answer questions about the model. This shows that the technology has the potential to assist in process model generation and iteration, at least for basic use cases. Hence, further research on the use of chatbots generating process models can be justified.  
What needs further research: More complex process modeling tasks, such as handling multi-party processes, message flows, and multi-pool models, still require further development. Additionally, there is a need for defining clear evaluation criteria regarding the quality of the generated models. Note that this goes beyond simple accuracy and touches on aspects like model comprehensibility, correctness, and suitability for practical use. To ensure the accuracy of chatbot-generated models, it is crucial to implement rigorous validation processes, such as comparing chatbot outputs with expert-generated models and using benchmarking against established standards. Additionally, regular audits and incorporating feedback loops can help in refining the models over time. Concerns regarding data security and privacy are critical, requiring robust encryption methods and adherence to data protection regulations to safeguard sensitive information. Additionally, implementing strict access controls, conducting regular security audits, and ensuring anonymization of data where possible can further enhance protection. It is also crucial to integrate secure data handling practices and maintain transparency with users about data usage policies to build trust and ensure compliance with privacy laws. We believe that these initial results confirm the potential of chatbot-based process model generation but also highlight the need for further research to evaluate the quality of the models and to develop methodologies to handle more complex scenarios. In summary, while our proof-of-concept experiment shows that chatbots can indeed generate process models from natural language text and interactively refine them, further research is needed to fully unlock their potential and ensure the quality and accuracy of the resulting models.