Validity Arguments for AI-Based Automated Scores: Essay Scoring as an Illustration

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Steve Ferrara, Saed Qunbar
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引用次数: 4

Abstract

In this article, we argue that automated scoring engines should be transparent and construct relevant—that is, as much as is currently feasible. Many current automated scoring engines cannot achieve high degrees of scoring accuracy without allowing in some features that may not be easily explained and understood and may not be obviously and directly relevant to the target assessment construct. We address the current limitations on evidence and validity arguments for scores from automated scoring engines from the points of view of the Standards for Educational and Psychological Testing (i.e., construct relevance, construct representation, and fairness) and emerging principles in Artificial Intelligence (e.g., explainable AI, an examinee's right to explanations, and principled AI). We illustrate these concepts and arguments for automated essay scores.

基于人工智能的自动评分的有效性论证:以论文评分为例
在本文中,我们认为自动评分引擎应该是透明的和结构相关的——也就是说,尽可能多的是当前可行的。如果不考虑一些可能不容易解释和理解的特征,并且可能与目标评估结构不明显和直接相关,那么许多当前的自动评分引擎无法实现高度的评分准确性。我们从教育和心理测试标准(即构建相关性,构建代表性和公平性)和人工智能新兴原则(例如,可解释的人工智能,考生的解释权和原则性人工智能)的角度解决了自动评分引擎得分的证据和有效性论点的当前限制。我们举例说明这些概念和论点自动作文分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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