01/2026 Generative Artificial Intelligence and Conceptual Modeling

Published : 20.06.2024 | Categories: Call For Papers


Since the introduction of the GPT-3 model and its open availability via ChatGPT in 2022 (Wu et al., 2023), artificial intelligence has received another boost of interest, both in the scientific research community and for practical applications. In particular, the sub-field of generative artificial intelligence has since then been at the focus of attention in many domains. At its core, generative artificial intelligence creates synthetic artifacts based on training samples from which it learns their patterns (Jovanović and Campell, 2022). Besides the already long-established generative adversarial networks (Goodfellow et al., 2014, Creswell et al., 2018), approaches using generative pre-trained transformer (GPT) models with multiple billions of parameters (Brown et al., 2020) and diffusion probabilistic models (Ho et al., 2020) dominate applications today. As of today, publicly available services are offered based on models that can take as input any combination of text / code, audio, image, and video and produce any combination of text, image, and audio output (OpenAI, 2024; Gemini Team, 2023).

In business and information systems engineering, there has been early investigation and adoption of these technological opportunities (Teubner et al., 2023). It has been found that generative artificial intelligence is already having an impact in various subfields of the community, including economics of information systems, enterprise modeling and enterprise engineering, human computer interaction, and social computing, as well as information systems engineering and technology (Feuerriegel et al., 2024). In the field of enterprise modeling and the broader field of conceptual modeling, it has been found early on that generative AI and in particular large language models can considerably change the way how models are created and interpreted (Fill et al., 2023; Cámara et al., 2023). This also extends to tasks such as requirements engineering (Ronanki et al., 2023), business process management (van der Aalst, 2023), software engineering (Fill et al., 2024), or semantic systems engineering (Buchmann et al., 2024). Thereby, the use of generative artificial intelligence has the potential to re-conceive the foundations of the field of conceptual modeling by considerably easing the way how models are derived from natural language, processed, and analyzed. At the same time, conceptual modeling has been considered as complement for machine learning approaches to structure requirements in this area (Maass and Storey, 2021).

With this call for papers we solicit papers presenting mature results on the interface of generative artificial intelligence and conceptual modeling. In this regard, we seek contributions both for using generative AI in the creation and processing of conceptual models as well as conceptual models for their use in the field of generative AI, e.g., to support the definition of machine learning workflows, to capture information systems design containing generative AI, or for explainability. Contributions have to adhere to well-established research methodologies in business and information systems engineering, computer science or related fields and make theoretical contributions to the state-of-the-art.

Papers that focus solely on generative artificial intelligence without a focus on conceptual modeling are not the focus of this call for papers.

Possible research areas include, but are not limited to:

  • Elicitation and Creation: Automated and semi-automated creation of conceptual models using generative artificial intelligence
  • Interpretation and Communication: Use of generative artificial intelligence for interpreting conceptual model contents and supporting communication
  • Processing and Execution: Derivation of executable models, processing scripts, and configuration of model engines using generative artificial intelligence
  • Transformation and Querying: Transformation and querying of conceptual models to different formats using generative artificial intelligence
  • Error detection and improvement: Using generative artificial intelligence for detecting errors in models and improving models as well as with property checking and formal model analysis
  • Configuration and Explainability: Use of conceptual models for configuring generative artificial intelligence and for explaining its outputs in formal or semi-formal manner
  • Information System Design and Understanding: Use of conceptual models in information system design when such systems contain generative AI elements
  • Combinations of Generative AI and Model-Checking to guarantee the correctness of generated models.


  • Conceptual/theoretical articles (also formal models and simulations)
  • Qualitative studies (e.g., interviews and case studies)
  • Quantitative studies (e.g., surveys, lab and field experiments, and trace data)
  • Design science and Engineering-oriented methods (i.e. artifacts)
  • Combinations of these approaches (i.e., multi- and mixed-methods)


All papers must be submitted by 15 January 2025 at the latest via the journal’s online submission system (http://www.editorialmanager.com/buis/). Please observe the instructions regarding the format and size of submission to BISE. Papers should adhere to the submission general BISE author guidelines (https://www.bise-journal.com/?page_id=18).

Submissions will be reviewed anonymously in a double-blind process by at least two referees with regard to relevance, originality, and research quality. In addition to the editors of the journal, distinguished international scholars will be involved in the review process.

Given the timeliness and importance of this topic, we aim to publish meaningful contributions after fast and limited decision cycles. The editorial timeline will proceed as follows:

  • Deadline for Submission: 15 Jan 2025
  • Notification of the Authors, 1st Round: 15 Apr 2025
  • Completion Revision 1: 15 Jul 2025
  • Notification of the Authors, 2nd Round: 1 Sep 2025
  • Completion Revision 2: 1 Oct 2025
  • Notification of the Authors, Final Round: 15 Oct 2025
  • Online Publication: asap
  • Print Publication: January 2026

Editors of the Special Issue

  • Hans-Georg Fill, University of Fribourg
  • Jennifer Horkoff, Chalmers University of Technology
  • Peter Fettke, DFKI, Saarland University
  • Julius Köpke, University of Klagenfurt


Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal,
Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell, Amanda, Agarwal,
Sandhini, Herbert-Voss, Ariel, Krueger, Gretchen, Henighan, Tom, Child, Rewon, Ramesh,
Aditya, Ziegler, Daniel M., Wu, Jeffrey, Winter, Clemens, Hesse, Christopher, Chen, Mark,
Sigler, Eric, Litwin, Mateusz, Gray, Scott, Chess, Benjamin, Clark, Jack, Berner, Christopher,
McCandlish, Sam, Radford, Alec, Sutskever, Ilya, Amodei, Dario. (2020): Language models
are few-shot learners, Advances in Neural Information Processing Systems, 33

Buchmann, Robert, Eder, Johann, Fill, Hans-Georg, Frank, Ulrich, Karagiannis, Dimitris and
Laurenzi, Emanuele, Mylopoulos, John, Plexousakis, Dimitris, Santos, Maribel Yasmina
(2024): Large language models: Expectations for semantics-driven systems engineering,
Data and Knowledge Engineering, DOI: 10.1016/j.datak.2024.102324

Cámara, Javier, Troya, Javier, Burgueno, Lola, Vallecillo, Antonio (2023): On the assessment
of generative AI in modeling tasks: an experience report with ChatGPT and UML, Software
and Systems Modeling, Vol. 22, 3, 781-793.

Creswell, Antonia, White, Tom, Dumoulin, Vincent, Arulkumaran, Kai, Sengupta, Biswa,
Bharath, Anil A (2018): Generative adversarial networks: An overview, IEEE signal
processing magazine, Vol. 35, Issue 1, 53-65.

Feuerriegel, Stefan, Hartmann, Jochen, Janiesch, Christian, Zschech, Patrick (2024):
Generative AI, Business and Information Systems Engineering, Vol. 66, 1, 111-126.

Fill, Hans-Georg, Fettke, Peter, Köpke, Julius (2023): Conceptual modeling and large
language models: impressions from first experiments with ChatGPT, Enterprise Modelling
and Information Systems Architectures – International Journal of Conceptual Modeling
(EMISAJ), Vol. 18, 1-15.

Fill, Hans-Georg, Cabot, Jordi, Maass, Wolfgang, Van Sinderen, Marten (2024): AI-Driven Software Engineering – The Role of Conceptual Modeling, Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling, Vol. 19, DOI: https://doi.org/10.18417/emisa.19.1

Gemini Team, Google (2023): Gemini: A family of highly capable multimodal models, arXiv,
URL: https://arxiv.org/abs/2312.11805

Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair,
Sherjil, Courville, Aaron, Bengio, Yoshua (2014): Generative adversarial nets, Advances in
neural information processing systems, 27.

Ho, Jonathan, Jain, Ajay, Abbeel, Pieter (2020): Denoising diffusion probabilistic models,
Advances in neural information processing systems, 33.

Maass, Wolfgang, Storey, Veda C (2021): Pairing conceptual modeling with machine
learning, Data and Knowledge Engineering, Vol. 134, Elsevier.

OpenAI (2024): Hello GPT-4o, May 13, 2024, OpenAI, URL: https://openai.com/index/hello-gpt-4o/
Ronanki, Krishna, Berger, Christian, Horkoff, Jennifer (2023): Investigating ChatGPT’s
Potential to Assist in Requirements Elicitation Processes, 49th Euromicro Conference on
Software Engineering and Advanced Applications, IEEE, 354-361.

Teubner, Timm, Flath, Christoph M, Weinhardt, Christof, van der Aalst, Wil, Hinz, Oliver
(2023): Welcome to the era of chatgpt et al. the prospects of large language models,
Business and Information Systems Engineering, Vol. 65, 2, 95-101.

Van der Aalst, Wil (2023): Process Management after ChatGPT: How Generative and
Predictive AI Relate to Process Mining, LinkedIn Pulse.

Wu, Tianyu, He, Shizhu, Liu, Jingping, Sun, Siqi, Liu, Kang, Han, Qing-Long, Tang, Yang
(2023): A brief overview of ChatGPT: The history, status quo and potential future
development, IEEE Journal of Automatica Sinica, Vol. 10, 5, 1122-1136.