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Electronic Education Machine AGI-EEdu
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).ORCID iD: 0000-0003-1611-5825
2023 (English)In: Artificial General Intelligence / [ed] Hammer, Patrick; Alirezaie, Marjan; Strannegård, Claes, Springer Nature , 2023, p. 265-275Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer Nature , 2023. p. 265-275
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13921
National Category
Pedagogy
Identifiers
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
Conference
Artificial General Intelligence: 16th International Conference, AGI 2023, Stockholm, Sweden, June 16–19, 2023
Note

QC 20230613

Part of ISBN 978-303133468-9

Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-11-05Bibliographically approved

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Subasic, Nihad

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