Transforming Assessment: The Impacts and Implications of Large Language Models and Generative AI

被引:18
|
作者
Hao, Jiangang [1 ]
von Davier, Alina A. [2 ]
Yaneva, Victoria [3 ]
Lottridge, Susan [4 ]
von Davier, Matthias [5 ]
Harris, Deborah J. [6 ]
机构
[1] Educ Testing Serv, Princeton, NJ 08541 USA
[2] Duolingo Inc, Pittsburgh, PA USA
[3] Natl Board Med Examiners, Philadelphia, PA USA
[4] Cambium Assessment Inc, Washington, DC USA
[5] Boston Coll, Chestnut Hill, MA 02467 USA
[6] Univ Iowa, Iowa City, IA USA
关键词
assessment; generative AI; LLMs; SUPPORT; TIME;
D O I
10.1111/emip.12602
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The remarkable strides in artificial intelligence (AI), exemplified by ChatGPT, have unveiled a wealth of opportunities and challenges in assessment. Applying cutting-edge large language models (LLMs) and generative AI to assessment holds great promise in boosting efficiency, mitigating bias, and facilitating customized evaluations. Conversely, these innovations raise significant concerns regarding validity, reliability, transparency, fairness, equity, and test security, necessitating careful thinking when applying them in assessments. In this article, we discuss the impacts and implications of LLMs and generative AI on critical dimensions of assessment with example use cases and call for a community effort to equip assessment professionals with the needed AI literacy to harness the potential effectively.
引用
收藏
页码:16 / 29
页数:14
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