{"title":"人工智能与形成性评估:火车已经发站了","authors":"Xiaoming Zhai, Ross H. Nehm","doi":"10.1002/tea.21885","DOIUrl":null,"url":null,"abstract":"<p>In response to Li, Reigh, He, and Miller's commentary, <i>Can we and should we use artificial intelligence for formative assessment in science</i>, 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.</p>","PeriodicalId":48369,"journal":{"name":"Journal of Research in Science Teaching","volume":"60 6","pages":"1390-1398"},"PeriodicalIF":3.6000,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.21885","citationCount":"9","resultStr":"{\"title\":\"AI and formative assessment: The train has left the station\",\"authors\":\"Xiaoming Zhai, Ross H. Nehm\",\"doi\":\"10.1002/tea.21885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In response to Li, Reigh, He, and Miller's commentary, <i>Can we and should we use artificial intelligence for formative assessment in science</i>, 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.</p>\",\"PeriodicalId\":48369,\"journal\":{\"name\":\"Journal of Research in Science Teaching\",\"volume\":\"60 6\",\"pages\":\"1390-1398\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.21885\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Research in Science Teaching\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/tea.21885\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research in Science Teaching","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/tea.21885","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
AI and formative assessment: The train has left the station
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.
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.