{"title":"AI or Human? Finding and Responding to Artificial Intelligence in Student Work","authors":"Gary D. Fisk","doi":"10.1177/00986283241251855","DOIUrl":null,"url":null,"abstract":"IntroductionRecent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity.Statement of the problemBoth humans and AI detection technologies have difficulty distinguishing between AI-generated vs. human-authored text. This weakness raises a significant possibility of false positive errors: human-authored writing incorrectly judged as AI-generated.Literature reviewAI detection methodology, whether machine or human-based, is based on writing style characteristics. Empirical evidence demonstrates that AI detection technologies are more sensitive to AI-generated text than human judges, yet a positive finding from these technologies cannot provide absolute certainty of AI plagiarism.Teaching implicationsGiven the uncertainty of detecting AI, a forgiving, pro-growth response to AI academic integrity cases is recommended, such as revise and resubmit decisions.ConclusionFaculty should cautiously embrace the use of AI detection technologies with the understanding that false positive errors will occasionally occur. This use is ethical provided that the responses to problematic cases are approached with the goal of educational growth rather than punishment.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/00986283241251855","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
IntroductionRecent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity.Statement of the problemBoth humans and AI detection technologies have difficulty distinguishing between AI-generated vs. human-authored text. This weakness raises a significant possibility of false positive errors: human-authored writing incorrectly judged as AI-generated.Literature reviewAI detection methodology, whether machine or human-based, is based on writing style characteristics. Empirical evidence demonstrates that AI detection technologies are more sensitive to AI-generated text than human judges, yet a positive finding from these technologies cannot provide absolute certainty of AI plagiarism.Teaching implicationsGiven the uncertainty of detecting AI, a forgiving, pro-growth response to AI academic integrity cases is recommended, such as revise and resubmit decisions.ConclusionFaculty should cautiously embrace the use of AI detection technologies with the understanding that false positive errors will occasionally occur. This use is ethical provided that the responses to problematic cases are approached with the goal of educational growth rather than punishment.
导言最近人工智能(AI)生成技术的创新导致教育环境中无法假定作者为人类,从而对维护学术诚信构成了重大挑战。问题陈述人类和 AI 检测技术都难以区分 AI 生成的文本与人类撰写的文本。文献综述无论是基于机器还是人类的人工智能检测方法,都是以写作风格特征为基础的。教学启示鉴于人工智能检测的不确定性,建议对人工智能学术诚信案例采取宽容的、有利于成长的应对措施,如修改并重新提交决定。只要以教育成长而不是惩罚为目标来应对有问题的案例,这种使用就是合乎道德的。