%Aigaion2 BibTeX export von HES SO Valais Publications
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@INPROCEEDINGS{Martinez2023,
     author = {Martinez, Fernanda and Collarana, Diego and Calvaresi, Davide and Arispe, Martin and Florida, Carla and Calbimonte, Jean-Paul},
     editor = {Pesquita, Catia and Skaf-Molli, Hala and Efthymiou, Vasilis and Kirrane, Sabrina and Ngonga, Axel and Collarana, Diego and Cerqueira, Renato and Alam, Mehwish and Trojahn, Cassia and Hertling, Sven},
   keywords = {Knowledge Graphs, NLP, Personalized Education},
      title = {Study-Buddy: A Knowledge Graph-Powered Learning Companion for School Students},
  booktitle = {The Semantic Web: ESWC 2023 Satellite Events},
     series = {Lecture Notes in Computer Science},
       year = {2023},
      pages = {133--137},
  publisher = {Springer Nature Switzerland},
    address = {Cham},
       isbn = {9783031434587},
        doi = {10.1007/978-3-031-43458-7_25},
   abstract = {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.},
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}},
}