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

IF 2.7 4区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Jiangang Hao, Alina A. von Davier, Victoria Yaneva, Susan Lottridge, Matthias von Davier, Deborah J. Harris
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引用次数: 0

Abstract

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.

改变评估:大型语言模型和生成式人工智能的影响和意义
以 ChatGPT 为代表的人工智能(AI)取得了长足进步,为评估领域带来了大量机遇和挑战。将尖端的大型语言模型(LLMs)和生成式人工智能应用于评估,在提高效率、减少偏差和促进定制化评估方面大有可为。与此相反,这些创新技术在有效性、可靠性、透明度、公平性、公正性和测试安全性等方面也引起了极大的关注,因此在评估中应用这些技术时必须慎重考虑。在这篇文章中,我们通过使用实例讨论了 LLMs 和生成式人工智能对评估关键维度的影响和意义,并呼吁社会各界共同努力,使评估专业人员具备必要的人工智能素养,从而有效利用人工智能的潜力。
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
3.90
自引率
15.00%
发文量
47
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