AI and formative assessment: The train has left the station

IF 3.6 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Xiaoming Zhai, Ross H. Nehm
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引用次数: 9

Abstract

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.

人工智能与形成性评估:火车已经发站了
针对Li, Reigh, He和Miller的评论“我们是否可以以及应该使用人工智能进行科学的形成性评估”,我们认为人工智能(AI)已经被广泛应用于各种教育环境的形成性评估。在同意Li等人呼吁进一步研究与人工智能相关的公平问题的同时,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等人提出的形成性评估的限制性观点提出了挑战,强调了人工智能在向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整个形成性评估实践;没有一个单一的评估工具可以捕捉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,并强调了在提到偏见时经验检验和基于证据的论点的重要性。我们断言,基于人工智能的形成性评估并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,我们讨论了人工智能如何促进评估实践中代表性模式的多样化,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,到认识到它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环境的人工智能系统的开发。最后,我们强调了解决人工智能偏见的重要性,理解其影响,并为基于人工智能的形成性评估制定最佳实践指南。
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来源期刊
Journal of Research in Science Teaching
Journal of Research in Science Teaching EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
8.80
自引率
19.60%
发文量
96
期刊介绍: Journal of Research in Science Teaching, the official journal of NARST: A Worldwide Organization for Improving Science Teaching and Learning Through Research, publishes reports for science education researchers and practitioners on issues of science teaching and learning and science education policy. Scholarly manuscripts within the domain of the Journal of Research in Science Teaching include, but are not limited to, investigations employing qualitative, ethnographic, historical, survey, philosophical, case study research, quantitative, experimental, quasi-experimental, data mining, and data analytics approaches; position papers; policy perspectives; critical reviews of the literature; and comments and criticism.
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