Leveraging AI in auditing: Exploring PCAOB deficiencies with ChatGPT

Q1 Social Sciences

Journal of Accounting Education Pub Date : 2025-09-01 Epub Date: 2025-10-08 DOI:10.1016/j.jaccedu.2025.100985

Amanda S. Marcy , Douglas M. Boyle , Ahmed A. Gomaa , Yibai Li

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引用次数: 0

Abstract

This case introduces you to the use of Large Language Models (LLMs), such as ChatGPT, in auditing, focusing on their application in evaluating Public Company Accounting Oversight Board (PCAOB) inspection reports. You will use chatbot technology to analyze audit deficiencies, identify related PCAOB Auditing Standards, identify affected financial statement accounts or disclosures, and summarize trends across multiple years. Additionally, you will use chatbot technology to generate and critique memos for firm leadership, highlighting strengths, limitations, and areas for improvement in AI-generated outputs. The case emphasizes developing technical knowledge of PCAOB processes and chatbot technology while fostering skills in prompt engineering, evaluation of AI-generated output, and professional writing. By requiring you to document prompts and outputs, the case ensures originality and encourages iterative learning. This case offers an innovative approach to integrating emerging technologies into accounting education, preparing you for the future of auditing.

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在审计中利用人工智能：利用ChatGPT探索PCAOB的缺陷

本案例向您介绍了大型语言模型（llm）（如ChatGPT）在审计中的使用，重点介绍了它们在评估公共公司会计监督委员会（PCAOB）检查报告中的应用。您将使用聊天机器人技术来分析审计缺陷，识别相关的PCAOB审计标准，识别受影响的财务报表账户或披露，并总结多年来的趋势。此外，您将使用聊天机器人技术为公司领导层生成和批评备忘录，突出ai生成输出的优势、局限性和改进领域。该案例强调开发PCAOB流程和聊天机器人技术的技术知识，同时培养快速工程、评估人工智能生成的输出和专业写作方面的技能。通过要求您记录提示和输出，案例确保了原创性并鼓励迭代学习。本案例提供了一种创新的方法，将新兴技术整合到会计教育中，为您的未来审计做好准备。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Accounting Education Social Sciences-Education

CiteScore

4.20

自引率

0.00%

发文量

期刊介绍： The Journal of Accounting Education (JAEd) is a refereed journal dedicated to promoting and publishing research on accounting education issues and to improving the quality of accounting education worldwide. The Journal provides a vehicle for making results of empirical studies available to educators and for exchanging ideas, instructional resources, and best practices that help improve accounting education. The Journal includes four sections: a Main Articles Section, a Teaching and Educational Notes Section, an Educational Case Section, and a Best Practices Section. Manuscripts published in the Main Articles Section generally present results of empirical studies, although non-empirical papers (such as policy-related or essay papers) are sometimes published in this section. Papers published in the Teaching and Educational Notes Section include short empirical pieces (e.g., replications) as well as instructional resources that are not properly categorized as cases, which are published in a separate Case Section. Note: as part of the Teaching Note accompany educational cases, authors must include implementation guidance (based on actual case usage) and evidence regarding the efficacy of the case vis-a-vis a listing of educational objectives associated with the case. To meet the efficacy requirement, authors must include direct assessment (e.g grades by case requirement/objective or pre-post tests). Although interesting and encouraged, student perceptions (surveys) are considered indirect assessment and do not meet the efficacy requirement. The case must have been used more than once in a course to avoid potential anomalies and to vet the case before submission. Authors may be asked to collect additional data, depending on course size/circumstances.