Supporting inclusive science learning through machine learning

The AIISE framework

authored by

Marvin Roski, Anett Hoppe, Andreas Nehring

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.

Organisation(s)

Chemistry Education Section

External Organisation(s)

German National Library of Science and Technology (TIB)

Type

Contribution to book/anthology

Pages

547-567

No. of pages

21

Publication date

24.10.2024

Publication status

Published

Peer reviewed

Yes

Sustainable Development Goals

SDG 4 - Quality Education

Electronic version(s)

https://doi.org/10.1093/oso/9780198882077.003.0024 (Access: Open)