Investigating AI languages’ ability to solve undergraduate finance problems

IF 1.3 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Education for Business Pub Date : 2023-09-28 DOI:10.1080/08832323.2023.2253963

Changyu Yang, Adam Stivers

{"title":"Investigating AI languages’ ability to solve undergraduate finance problems","authors":"Changyu Yang, Adam Stivers","doi":"10.1080/08832323.2023.2253963","DOIUrl":null,"url":null,"abstract":"AbstractThe rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI language models—ChatGPT and Google Bard—focusing on their ability to solve undergraduate finance problems. We find that GPT-4 significantly outperforms Bard-1.0, excelling in easy problems but struggling with complex ones. The results suggest that it is crucial to handle AI with care in order to uphold academic integrity.Keywords: Artificial intelligenceChatGPTfinancial educationhigher educationundergraduate finance AcknowledgmentsThe authors would like to thank Shishir Paudel, Shiang Liu, and Taggert Brooks for their help.Disclosure statementThe authors report there are no competing interests to declare.Additional informationFundingThis work was supported by grants from the University of Wisconsin-La Crosse College of Business Administration and Menard Family Midwest Initiative for Economic Engagement and Research.","PeriodicalId":47318,"journal":{"name":"Journal of Education for Business","volume":"97 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Education for Business","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/08832323.2023.2253963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}

引用次数: 0

Abstract

AbstractThe rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI language models—ChatGPT and Google Bard—focusing on their ability to solve undergraduate finance problems. We find that GPT-4 significantly outperforms Bard-1.0, excelling in easy problems but struggling with complex ones. The results suggest that it is crucial to handle AI with care in order to uphold academic integrity.Keywords: Artificial intelligenceChatGPTfinancial educationhigher educationundergraduate finance AcknowledgmentsThe authors would like to thank Shishir Paudel, Shiang Liu, and Taggert Brooks for their help.Disclosure statementThe authors report there are no competing interests to declare.Additional informationFundingThis work was supported by grants from the University of Wisconsin-La Crosse College of Business Administration and Menard Family Midwest Initiative for Economic Engagement and Research.

查看原文本刊更多论文

研究人工智能语言解决大学生财务问题的能力

摘要人工智能(AI)的快速发展已经产生了复杂的语言模型，这些模型擅长于理解和生成类似人类的文本。这些模型具有处理大量信息的能力，可以有效地解决不同领域的问题。在本文中，我们对著名的人工智能语言模型——chatgpt和b谷歌bard进行了比较分析，重点关注它们解决大学生金融问题的能力。我们发现GPT-4明显优于Bard-1.0，在简单问题上表现出色，但在复杂问题上表现不佳。结果表明，为了维护学术诚信，谨慎对待人工智能至关重要。关键词:人工智能;人工智能;金融教育;高等教育;作者报告无利益竞争需要申报。本研究得到了威斯康星大学拉克罗斯工商管理学院和梅纳德家庭中西部经济参与与研究倡议的资助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Education for Business EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

3.10

自引率

8.30%

发文量

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.