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
traditional assessment methods like multiple-choice questions, which often fail
to capture the complexities of scientific thinking and three-dimensional
learning in science. It emphasizes the need for performance-based assessments
that require students to engage in scientific practices like modeling,
explanation, and argumentation. The paper achieves three major goals: reviewing
the current state of ML-based assessments in science education, introducing a
framework for scoring accuracy in ML-based automatic assessments, and
discussing future directions and challenges. It delves into the evolution of
ML-based automatic scoring systems, discussing various types of ML, like
supervised, unsupervised, and semi-supervised learning. These systems can
provide timely and objective feedback, thus alleviating the burden on teachers.
The paper concludes by exploring pre-trained models like BERT and finetuned
ChatGPT, which have shown promise in assessing students' written responses
effectively.

本文着重探讨人工智能（AI）和机器学习（ML）在科学评估中的转变作用，强调科学评估需要从概念学习转向知识运用，并介绍了基于 ML 的科学评估的现状、准确性评分框架以及未来方向和挑战。