Drivers and barriers of learning MBSE: design and validation of a RAG-based AI chatbot leveraging smart views

Learning MBSE is hindered by abstraction and complex tools. This paper identifies barriers via literature review and interviews to design a RAG-based chatbot acting as a “smart view” for contextual guidance. Evaluated through a semester-long field study and a controlled experiment, the prototype shows high usability and reduces cognitive load. While performance is comparable to traditional e-books, the RAG-enabled system effectively mitigates entry-level barriers and aids authentic project work through stepwise tutoring, offering a scalable, interactive complement to MBSE education.

Subjects

model-based systems engineering (MBSE)

artificial intelligence (AI)

engineering education

large language model (LLM)

systems engineering (SE)

DDC Class

371.3: Methods of instruction and study

Lizenz

https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication version

publishedVersion

Name

drivers-and-barriers-of-learning-mbse-design-and-validation-of-a-rag-based-ai-chatbot-leveraging-smart-views.pdf

Type

Main Article

Size

557.51 KB

Format

Adobe PDF

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Drivers and barriers of learning MBSE: design and validation of a RAG-based AI chatbot leveraging smart views