创新教育评估中的有效性论证与人工智能

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Educational Measurement Pub Date : 2022-07-08 DOI:10.1111/jedm.12331
David W. Dorsey, Hillary R. Michaels
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引用次数: 0

摘要

通过技术进步,我们已经大大提高了我们在一系列用途中创建丰富、复杂和有效评估的能力。人工智能(AI)支持的评估代表了一个这样的进步领域——一个吸引了我们集体兴趣和想象力的领域。组织和劳动力评估领域的科学家和实践者越来越多地在评估中使用人工智能,并且它的使用现在在教育中变得越来越普遍。虽然这些类型的解决方案为用户提供了效率、有效性和“惊喜因素”的承诺,但用户需要在高风险设置中保持高的有效性和公平性标准。由于一些人工智能方法和工具的复杂性,这种遵守标准的要求可能会挑战我们构建有效性和公平性论点的传统方法。在这个版本中,我们回顾了在教育评估领域,当有效性争论遇到人工智能时,这些挑战可能看起来像什么。我们特别探讨了人工智能如何影响以证据为中心的设计(ECD),以及从评估概念和编码到评分和报告的发展。我们还提供了有关确保这些系统不存在偏见的方法的信息。最后,我们讨论了未来的前景,其中许多即将到来,以最大限度地发挥人工智能的作用,同时最大限度地减少对考生和项目的负面影响。
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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.

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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
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.
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