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Revisiting BPMN Assignments with AI in Mind: Insights from Experiments with Large Language Models in Process Modeling Education

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F25%3A43933083" target="_blank" >RIV/60461373:22340/25:43933083 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/content/pdf/10.1007/978-3-032-02936-2.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007/978-3-032-02936-2.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-032-02936-2_25" target="_blank" >10.1007/978-3-032-02936-2_25</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Revisiting BPMN Assignments with AI in Mind: Insights from Experiments with Large Language Models in Process Modeling Education

  • Original language description

    As Large Language Models (LLMs) become increasingly available to students, BPM educators face a new challenge: how to design assignments that remain pedagogically effective and resistant to superficial AI-generated answers. This paper presents the results of two experiments that simulate common BPMN-related homework tasks and test how LLMs respond to them. The first experiment focused on answering comprehension questions based on five BPMN models, each provided either in PNG or XML format. The second asked the models to detect modeling errors in 30 flawed BPMN diagrams. In both cases, we evaluated the outputs of ChatGPT-4o and Gemini Flash, analyzing the correctness, reasoning, and completeness of their responses. Our findings show that while current LLMs are not yet fully capable of reliably solving BPMN assignments-especially those involving deeper process logic-they can already provide partially correct and plausible responses. This raises questions about the future of BPM education, the design of AI-aware assignments, and the role of LLMs as potential learning assistants. Alongside insights and lessons learned, we provide materials to help instructors adapt their teaching to an AI-enabled environment.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Lecture Notes in Business Information Processing

  • ISBN

    978-3-031-70444-4

  • ISSN

    1865-1348

  • e-ISSN

    1865-1356

  • Number of pages

    15

  • Pages from-to

    358-372

  • Publisher name

    Springer Science and Business Media Deutschland GmbH

  • Place of publication

    Cham

  • Event location

    AGH Univ Krakow

  • Event date

    Sep 1, 2024

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001588122500023