{"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.
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.