Jiangang Hao, Alina A. von Davier, Victoria Yaneva, Susan Lottridge, Matthias von Davier, Deborah J. Harris
{"title":"Transforming Assessment: The Impacts and Implications of Large Language Models and Generative AI","authors":"Jiangang Hao, Alina A. von Davier, Victoria Yaneva, Susan Lottridge, Matthias von Davier, Deborah J. Harris","doi":"10.1111/emip.12602","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":47345,"journal":{"name":"Educational Measurement-Issues and Practice","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Measurement-Issues and Practice","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/emip.12602","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
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.