A Transparent and Adaptive AI Assistant for Teaching Knowledge Engineering

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

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Abstract

As generative AI tools become more widespread, students are increasingly using them for assistance with complex tasks such as modeling ontology constraints. While the success of large language models have been widely explored, in-use applications remain underdeveloped, and experimental findings are often inaccessible to students or novice engineers. As a result, learners do not fully benefit from AI-assisted support or fail to critically engage with AI generated outputs. To bridge this gap, we propose a transparent, research-informed AI Assistant framework that follows hybrid intelligence principles and aims to support Knowledge Engineering education, with a focus on modeling logical ontology constraints. Preliminary results suggest that such a system can improve the accuracy of student-generated ontology models by over 10%.
Original languageEnglish
Title of host publicationWOP-HAIBRIDGE 2025 Joint Proceedings WOP and HAIBRIDGE 2025
Subtitle of host publicationJoint Proceedings of the 16th Workshop on Ontology Design and Patterns and the 1st Workshop on Bridging Hybrid Intelligence and the Semantic Web (WOP-HAIBRIDGE 2025) co-located with the 24th International Semantic Web Conference (ISWC 2025)
Number of pages5
ISBN (Electronic)1613-0073
Publication statusPublished - 2025

Publication series

SeriesCEUR Workshop Proceedings
ISSN1613-0073

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