Now showing1 - 2 of 2
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Drivers and barriers of learning MBSE: design and validation of a RAG-based AI chatbot leveraging smart views(Cambridge University Press (CUP), 2026-07); ; ; ; 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.Publicationtype: Conference Paper28 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Large Language Model-supported approach for requirements extraction from different sources of reference system elements(The Design Society, 2026); ; ; ; This paper introduces an approach for LLM-supported requirements extraction from different data sources within SGE – System Generation Engineering. Developing new mechatronic system generations involves gathering requirements from heterogeneous artifacts, such as specifications, standards, competitor materials. Manual extraction is often time-consuming and error-prone. The proposed approach, developed through industry collaboration and academic projects, incorporates a five-step workflow comprising source identification, preparation, prompting, pre-check, and final check, as well as methodological support, such as a standardized prompt template and INCOSE criteria, to enable requirements extraction. Key features include applicability across different data sources, traceability, domain adaptability, reproducibility, expert validation, and scalability. An initial exploratory evaluation during a semester-long, practice-oriented live-lab indicates the approach’s applicability and potential benefits, such as reduced manual effort, traceability, and the possibility of using it as sparring partner. However, the evaluation also highlights challenges, such as the importance of human involvement to ensure context awareness and requirements quality.Publicationtype: Conference Paper7