AI and formative assessment: The train has left the station
AI and formative assessment: The train has left the station
复制标题
人工智能和形成性评估：火车已离站
DOI：
10.1002/tea.21885
复制
发表时间：
2023
影响因子：
4.6
通讯作者：
Nehm, Ross H.
中科院分区：
文献类型：
--
作者：
Zhai, Xiaoming;Nehm, Ross H.
针对李，Reigh，He和米勒的评论，我们可以和我们应该使用人工智能在科学形成性评估，我们认为人工智能（AI）已经被广泛应用于各种教育背景下的形成性评估。虽然同意李等人。我们呼吁进一步研究与人工智能相关的公平问题，我们强调科学教育工作者需要适应已经超过研究界的人工智能革命。我们对Li等人提出的关于形成性评价的限制性观点提出质疑，强调人工智能在为学生提供形成性反馈、协助教师进行评估实践和协助教学决策方面的重要贡献。我们认为，人工智能生成的分数不应等同于形成性评估实践的全部;没有一种评估工具可以捕捉到学生思维和背景的所有方面。我们解决了Li等人提出的关于人工智能偏见的问题，并强调了经验测试和基于证据的论点在提到偏见时的重要性。我们断言，基于人工智能的形成性评估不一定会导致不公平，事实上，它可以促进更公平的教育体验。此外，我们还讨论了人工智能如何促进评估实践中代表模式的多样化，并强调了人工智能在节省教师时间和为他们提供有价值的评估信息方面的潜在好处。我们呼吁转变观点，从将人工智能视为一个有待解决的问题，到认识到其作为教育协作工具的潜力。我们强调，未来的研究需要关注人工智能在课堂、教师教育中的有效整合，以及能够适应不同教学和学习环境的人工智能系统的开发。最后，我们强调了解决AI偏见的重要性，了解其影响，并为基于AI的形成性评估的最佳实践制定指导方针。
In response to Li, Reigh, He, and Miller's commentary,Can we and should we use artificial intelligence for formative assessment in science, we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has outpaced the research community. We challenge the somewhat restrictive view of formative assessment presented by Li et al., highlighting the significant contributions of AI in providing formative feedback to students, assisting teachers in assessment practices, and aiding in instructional decisions. We contend that AI‐generated scores should not be equated with the entirety of formative assessment practice; no single assessment tool can capture all aspects of student thinking and backgrounds. We address concerns raised by Li et al. regarding AI bias and emphasize the importance of empirical testing and evidence‐based arguments in referring to bias. We assert that AI‐based formative assessment does not necessarily lead to inequity and can, in fact, contribute to more equitable educational experiences. Furthermore, we discuss how AI can facilitate the diversification of representational modalities in assessment practices and highlight the potential benefits of AI in saving teachers’ time and providing them with valuable assessment information. We call for a shift in perspective, from viewing AI as a problem to be solved to recognizing its potential as a collaborative tool in education. We emphasize the need for future research to focus on the effective integration of AI in classrooms, teacher education, and the development of AI systems that can adapt to diverse teaching and learning contexts. We conclude by underlining the importance of addressing AI bias, understanding its implications, and developing guidelines for best practices in AI‐based formative assessment.
登录
查看更多内容
使用机器学习对化学和物理的多维评估进行评分
影响因子：
4.4
作者：
Sarah Maestrales;X. Zhai;Israel Touitou;Quinton Baker;Barbara Schneider;J. Krajcik
通讯作者：
J. Krajcik
DOI：
10.1007/s11412-019-09298-y
发表时间：
2019-09-01
影响因子：
4.3
作者：
Gerard, Libby;Kidron, Ady;Linn, Marcia C.
通讯作者：
Linn, Marcia C.
影响因子：
4.6
作者：
Zhai, Xiaoming;He, Peng;Krajcik, Joseph
通讯作者：
Krajcik, Joseph
影响因子：
4.4
作者：
Zhai, Xiaoming;Shi, Lehong;Nehm, Ross H.
通讯作者：
Nehm, Ross H.
影响因子：
--
作者：
Kayhan Moharreri;M. Ha;R. Nehm
通讯作者：
Kayhan Moharreri;M. Ha;R. Nehm