Study-Buddy: A Knowledge Graph-Powered Learning Companion for School Students
| Tipo de publicação: | Inproceedings |
| Citação: | Martinez2023 |
| Booktitle: | The Semantic Web: ESWC 2023 Satellite Events |
| Series: | Lecture Notes in Computer Science |
| Ano: | 2023 |
| Páginas: | 133--137 |
| Publisher: | Springer Nature Switzerland |
| Endereço: | Cham |
| ISBN: | 9783031434587 |
| DOI: | 10.1007/978-3-031-43458-7_25 |
| Resumo: | Large Language Models (LLMs) have the potential to substantially improve educational tools for students. However, they face limitations, including factual accuracy, personalization, and the lack of control over the sources of information. This paper presents Study-Buddy, a prototype of a conversational AI assistant for school students to address the above-mentioned limitations. Study-Buddy embodies an AI assistant based on a knowledge graph, LLMs models, and computational persuasion. It is designed to support educational campaigns as a hybrid AI solution. The demonstrator showcases interactions with Study-Buddy and the crucial role of the Knowledge Graph for the bot to present the appropriate activities to the students. A video demonstrating the main features of Study-Buddy is available at: https://youtu.be/DHPTsN1RI9o. |
| Campos do usuário: | file={Full Text PDF:https\://link.springer.com/content/pdf/10.1007%2F978-3-031-43458-7_25.pdf:application/pdf}, language={en}, shorttitle={Study-{Buddy}}, |
| Palavras-chave: | Knowledge Graphs, NLP, Personalized Education |
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| Adicionado por: | [] |
| Total mark: | 0 |
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