{"title":"创新教育评估中的有效性论证与人工智能","authors":"David W. Dorsey, Hillary R. Michaels","doi":"10.1111/jedm.12331","DOIUrl":null,"url":null,"abstract":"<p>We have dramatically advanced our ability to create rich, complex, and effective assessments across a range of uses through technology advancement. Artificial Intelligence (AI) enabled assessments represent one such area of advancement—one that has captured our collective interest and imagination. Scientists and practitioners within the domains of organizational and workforce assessment have increasingly used AI in assessment, and its use is now becoming more common in education. While these types of solutions offer their users the promise of efficiency, effectiveness, and a “wow factor,” users need to maintain high standards for validity and fairness in high stakes settings. Due to the complexity of some AI methods and tools, this requirement for adherence to standards may challenge our traditional approaches to building validity and fairness arguments. In this edition, we review what these challenges may look like as validity arguments meet AI in educational assessment domains. We specifically explore how AI impacts Evidence-Centered Design (ECD) and development from assessment concept and coding to scoring and reporting. We also present information on ways to ensure that bias is not built into these systems. Lastly, we discuss future horizons, many that are almost here, for maximizing what AI offers while minimizing negative effects on test takers and programs.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":"59 3","pages":"267-271"},"PeriodicalIF":1.4000,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validity Arguments Meet Artificial Intelligence in Innovative Educational Assessment\",\"authors\":\"David W. Dorsey, Hillary R. Michaels\",\"doi\":\"10.1111/jedm.12331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We have dramatically advanced our ability to create rich, complex, and effective assessments across a range of uses through technology advancement. Artificial Intelligence (AI) enabled assessments represent one such area of advancement—one that has captured our collective interest and imagination. Scientists and practitioners within the domains of organizational and workforce assessment have increasingly used AI in assessment, and its use is now becoming more common in education. While these types of solutions offer their users the promise of efficiency, effectiveness, and a “wow factor,” users need to maintain high standards for validity and fairness in high stakes settings. Due to the complexity of some AI methods and tools, this requirement for adherence to standards may challenge our traditional approaches to building validity and fairness arguments. In this edition, we review what these challenges may look like as validity arguments meet AI in educational assessment domains. We specifically explore how AI impacts Evidence-Centered Design (ECD) and development from assessment concept and coding to scoring and reporting. We also present information on ways to ensure that bias is not built into these systems. Lastly, we discuss future horizons, many that are almost here, for maximizing what AI offers while minimizing negative effects on test takers and programs.</p>\",\"PeriodicalId\":47871,\"journal\":{\"name\":\"Journal of Educational Measurement\",\"volume\":\"59 3\",\"pages\":\"267-271\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational Measurement\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jedm.12331\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PSYCHOLOGY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Measurement","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jedm.12331","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
Validity Arguments Meet Artificial Intelligence in Innovative Educational Assessment
We have dramatically advanced our ability to create rich, complex, and effective assessments across a range of uses through technology advancement. Artificial Intelligence (AI) enabled assessments represent one such area of advancement—one that has captured our collective interest and imagination. Scientists and practitioners within the domains of organizational and workforce assessment have increasingly used AI in assessment, and its use is now becoming more common in education. While these types of solutions offer their users the promise of efficiency, effectiveness, and a “wow factor,” users need to maintain high standards for validity and fairness in high stakes settings. Due to the complexity of some AI methods and tools, this requirement for adherence to standards may challenge our traditional approaches to building validity and fairness arguments. In this edition, we review what these challenges may look like as validity arguments meet AI in educational assessment domains. We specifically explore how AI impacts Evidence-Centered Design (ECD) and development from assessment concept and coding to scoring and reporting. We also present information on ways to ensure that bias is not built into these systems. Lastly, we discuss future horizons, many that are almost here, for maximizing what AI offers while minimizing negative effects on test takers and programs.
期刊介绍:
The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.