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Electronic Education Machine AGI-EEdu
KTH, Skolan för industriell teknik och management (ITM), Maskinkonstruktion (Inst.).ORCID-id: 0000-0003-1611-5825
2023 (engelsk)Inngår i: Artificial General Intelligence / [ed] Hammer, Patrick; Alirezaie, Marjan; Strannegård, Claes, Springer Nature , 2023, s. 265-275Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This project is part of the digitization of engineering education at the Royal Institute of Technology (KTH). The digitization process of the observed undergraduate course in electrical engineering so far allows students to use the Learning Management System (LMS) to find course literature and notes, recorded lectures, theoretical exercises in the form of quizzes, and online interactive calculation exercises with built-in feedback for each question. What remains to be solved is the human-type contact that can answer a student’s question, for example: “How am I doing in the course?”. Usually, an experienced teacher would be able to answer that question with his own predictions.

sted, utgiver, år, opplag, sider
Springer Nature , 2023. s. 265-275
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13921
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-328910DOI: 10.1007/978-3-031-33469-6_27ISI: 001319411700027Scopus ID: 2-s2.0-85163313891OAI: oai:DiVA.org:kth-328910DiVA, id: diva2:1766871
Konferanse
Artificial General Intelligence: 16th International Conference, AGI 2023, Stockholm, Sweden, June 16–19, 2023
Merknad

QC 20230613

Part of ISBN 978-303133468-9

Tilgjengelig fra: 2023-06-13 Laget: 2023-06-13 Sist oppdatert: 2024-11-05bibliografisk kontrollert

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Totalt: 179 treff
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