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.

中文翻译：

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人工智能和形成性评估：火车已离站

针对 Li、Reigh、He 和 Miller 的评论，我们可以并且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）已经广泛应用于各种教育环境的形成性评估中。在同意李等人关于进一步研究与人工智能相关的公平问题的呼吁的同时，我们强调科学教育者需要适应已经超过研究界的人工智能革命。我们对李等人提出的形成性评估的某种限制性观点提出了挑战，强调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以及协助教学决策方面的重大贡献。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；没有任何一种评估工具可以全面反映学生思维和背景。我们解决了 Li 等人提出的担忧。关于人工智能偏见，并强调实证测试和基于证据的论据在提及偏见时的重要性。我们断言，基于人工智能的形成性评估并不一定会导致不平等，事实上，可以有助于实现更公平的教育体验。此外，我们讨论了人工智能如何促进评估实践中表征模式的多样化，并强调人工智能在节省教师时间并为他们提供有价值的评估信息方面的潜在好处。我们呼吁转变视角，从将人工智能视为一个需要解决的问题，转变为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重点关注人工智能在课堂、教师教育、以及开发能够适应不同教学环境的人工智能系统。最后，我们强调解决人工智能偏见、理解其影响以及制定基于人工智能的形成性评估最佳实践指南的重要性。