Nodir Adilov, Jeffrey W. Cline, Hui Hanke, Kent Kauffman, Lisa Meneau, Elva Resendez, Shubham Singh, Mike Slaubaugh, Nichaya Suntornpithug
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引用次数: 0
Abstract
AbstractThis article develops an index to measure the level of susceptibility of courses to cheating using ChatGPT (Chat Generative Pre-trained Transformer), an advanced text-based artificial intelligence (AI) language model. It demonstrates the application of the index to a sample of business courses in a mid-sized university. The study finds that the vulnerability index varies across disciplines and teaching modalities. As advanced language models become more common in academic settings and create new educational challenges, the study provides an intuitive and practical mechanism for instructors and academic units to measure and assess the vulnerability of their courses to various language-based predictive models.Keywords: ChatGPTcheating using AIclassroom cheatingcourse vulnerability index AcknowledgmentsWe thank the participants of the Midwest Business Association Administration (MBAA) 2023 conference for their helpful suggestions and comments. We also thank Marc Lafuente for his assistance.Author contributionsConceptualization: N. Adilov.Analysis: N. Adilov, J. W. Cline, H. Hanke, K. Kauffman, L. Meneau, E. Resendez, S. Singh, M. Slaubaugh, N. Suntornpithug.Writing and editing: N. Adilov, J. W. Cline, H. Hanke, K. Kauffman, L. Meneau, E. Resendez, S. Singh, M. Slaubaugh, N. Suntornpithug.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 ChatGPT uses multiple layers of “transformers” to understand text, to recognize the patterns, and to predict the next word in the text. ChatGPT has been trained on vast amounts of text data and then “fine-tuned” to be more user-friendly when responding to queries.2 To the best of authors’ knowledge, no other researchers have utilized a similar course vulnerability index. Consequently, we took it upon ourselves to develop the index. However, Adilov and Cline (Citation2023) presented an economic theoretical model and suggested that the value of our course vulnerability index affects the amount of effort a student invests in a course.3 The VI could theoretically increase beyond 100 if an instructor offers extra credit points.4 Hybrid courses are approximately 50% face-to-face and 50% online.5 As an exception to this pattern, ChatGPT’s accuracy in business statistics was at 90%.6 Our recommendations are consistent with the theoretical findings of Adilov and Cline (Citation2023) that the percentage of course requirements where students are able to use AI to cheat should be minimized.
摘要本文利用ChatGPT(聊天生成预训练转换器)——一种先进的基于文本的人工智能(AI)语言模型,开发了一个指标来衡量课程对作弊的敏感性水平。本文以一所中等规模大学的商科课程为例,说明了该指数的应用。研究发现,不同学科、不同教学方式的学生脆弱性指数存在差异。随着先进的语言模型在学术环境中变得越来越普遍,并带来了新的教育挑战,该研究为教师和学术单位提供了一种直观和实用的机制,以衡量和评估他们的课程对各种基于语言的预测模型的脆弱性。关键词:chatgpt使用网络作弊课堂作弊课程漏洞指数感谢中西部商业协会管理局(MBAA) 2023年会议的与会者提供的宝贵建议和意见。我们也感谢马克·拉文特的协助。概念化:N. Adilov。分析:N. Adilov, J. W. Cline, H. Hanke, K. Kauffman, L. Meneau, E. Resendez, S. Singh, M. Slaubaugh, N. Suntornpithug撰稿编辑:N. Adilov, J. W. Cline, H. Hanke, K. Kauffman, L. Meneau, E. Resendez, S. Singh, M. Slaubaugh, N. Suntornpithug披露声明作者未报告潜在的利益冲突。注1 ChatGPT使用多层“变形器”来理解文本、识别模式并预测文本中的下一个单词。ChatGPT已经在大量的文本数据上进行了训练,然后进行了“微调”,使其在响应查询时更加用户友好据笔者所知,还没有其他研究人员使用过类似的课程漏洞指数。因此,我们自己承担了开发该指数的责任。然而,Adilov和Cline (Citation2023)提出了一个经济理论模型,并认为我们的课程脆弱性指数的值会影响学生在课程上投入的努力量如果老师提供额外的学分,理论上VI可以超过100分混合课程大约50%面对面授课,50%在线授课作为此模式的一个例外,ChatGPT在业务统计中的准确性为90%我们的建议与Adilov和Cline (Citation2023)的理论发现是一致的,即学生能够使用人工智能作弊的课程要求的百分比应该最小化。
期刊介绍:
The Journal of Education for Business is for those educating tomorrow''s businesspeople. The journal primarily features basic and applied research-based articles in entrepreneurship, accounting, communications, economics, finance, information systems, management, marketing, and other business disciplines. Along with the focus on reporting research within traditional business subjects, an additional expanded area of interest is publishing articles within the discipline of entrepreneurship. Articles report successful innovations in teaching and curriculum development at the college and postgraduate levels. Authors address changes in today''s business world and in the business professions that are fundamentally influencing the competencies that business graduates need. JEB also offers a forum for new theories and for analyses of controversial issues. Articles in the Journal fall into the following categories: Original and Applied Research; Editorial/Professional Perspectives; and Innovative Instructional Classroom Projects/Best Practices. Articles are selected on a blind peer-reviewed basis. Original and Applied Research - Articles published feature the results of formal research where findings have universal impact. Editorial/Professional Perspective - Articles published feature the viewpoint of primarily the author regarding important issues affecting education for business. Innovative Instructional Classroom Projects/Best Practices - Articles published feature the results of instructional experiments basically derived from a classroom project conducted at one institution by one or several faculty.