May 14, 2024, 4:42 a.m. | Xiaoming Zhai

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.06660v1 Announce Type: cross
Abstract: This chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align with the Framework's three dimensions: science and engineering practices, disciplinary core ideas, and crosscutting concepts. The paper further highlights the limitations of …

abstract ai and machine learning artificial artificial intelligence arxiv cs.ai cs.cy cs.lg development education framework intelligence k-12 knowledge machine machine learning next paper physics.ed-ph role science shift type

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