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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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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