Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Uses of Artificial Intelligence in STEM Education |
| Erscheinungsort | Oxford |
| Herausgeber (Verlag) | Oxford University Press |
| Kapitel | 24 |
| Seiten | 547-567 |
| Seitenumfang | 21 |
| ISBN (elektronisch) | 9780191991226 |
| ISBN (Print) | 9780198882077 |
| Publikationsstatus | Veröffentlicht - 24 Okt. 2024 |
Abstract
Integrating artificial intelligence (AI) and machine learning (ML) into science education offers the potential to improve teaching and learning processes. Alongside these developments, global education has evolved to include diverse learners by shifting from a disability-centered perspective to a broad understanding of inclusion, aiming at supporting all learners. Linking AI, science education, and inclusive pedagogy promises to understand and model individualized learning supported by ML and learning analytics to enable accessible learning experiences. In this chapter, the NinU-framework (proposed by the Network for Inclusive Science Education: NinU), which bridges inclusive pedagogy and science education, is linked with AI-based perspectives leading to the novel Artificial Intelligence in Inclusive Science Education (AIISE) framework. This chapter describes the AIISE framework and provides researchers with criteria to consider when addressing inclusivity and avoiding discrimination in ML-enhanced learning. It extends the established "NinU scheme" and provides a roadmap for integrating AI into inclusive science education.
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Uses of Artificial Intelligence in STEM Education. Oxford: Oxford University Press, 2024. S. 547-567.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Supporting inclusive science learning through machine learning
T2 - The AIISE framework
AU - Roski, Marvin
AU - Hoppe, Anett
AU - Nehring, Andreas
N1 - Publisher Copyright: © Oxford University Press 2024. All rights reserved.
PY - 2024/10/24
Y1 - 2024/10/24
N2 - Integrating artificial intelligence (AI) and machine learning (ML) into science education offers the potential to improve teaching and learning processes. Alongside these developments, global education has evolved to include diverse learners by shifting from a disability-centered perspective to a broad understanding of inclusion, aiming at supporting all learners. Linking AI, science education, and inclusive pedagogy promises to understand and model individualized learning supported by ML and learning analytics to enable accessible learning experiences. In this chapter, the NinU-framework (proposed by the Network for Inclusive Science Education: NinU), which bridges inclusive pedagogy and science education, is linked with AI-based perspectives leading to the novel Artificial Intelligence in Inclusive Science Education (AIISE) framework. This chapter describes the AIISE framework and provides researchers with criteria to consider when addressing inclusivity and avoiding discrimination in ML-enhanced learning. It extends the established "NinU scheme" and provides a roadmap for integrating AI into inclusive science education.
AB - Integrating artificial intelligence (AI) and machine learning (ML) into science education offers the potential to improve teaching and learning processes. Alongside these developments, global education has evolved to include diverse learners by shifting from a disability-centered perspective to a broad understanding of inclusion, aiming at supporting all learners. Linking AI, science education, and inclusive pedagogy promises to understand and model individualized learning supported by ML and learning analytics to enable accessible learning experiences. In this chapter, the NinU-framework (proposed by the Network for Inclusive Science Education: NinU), which bridges inclusive pedagogy and science education, is linked with AI-based perspectives leading to the novel Artificial Intelligence in Inclusive Science Education (AIISE) framework. This chapter describes the AIISE framework and provides researchers with criteria to consider when addressing inclusivity and avoiding discrimination in ML-enhanced learning. It extends the established "NinU scheme" and provides a roadmap for integrating AI into inclusive science education.
KW - Artificial intelligence
KW - Inclusive education
KW - Learning analytics
KW - Science education
UR - http://www.scopus.com/inward/record.url?scp=85186661439&partnerID=8YFLogxK
U2 - 10.1093/oso/9780198882077.003.0024
DO - 10.1093/oso/9780198882077.003.0024
M3 - Contribution to book/anthology
AN - SCOPUS:85186661439
SN - 9780198882077
SP - 547
EP - 567
BT - Uses of Artificial Intelligence in STEM Education
PB - Oxford University Press
CY - Oxford
ER -