This paper investigates how artificial intelligence (AI) is reshaping the accounting industry through our proposed Accounting Tasks and Levels of AI Suitability (ATLAS) framework. As a conceptual framework, it systematically maps AI methodologies to accounting processes based on cognitive requirements, analyzing the varying susceptibility of tasks to automation with implications for accounting practice and the accounting workforce. The analysis suggests that rule-based activities like invoice processing appear highly amenable to automation, whereas tasks requiring professional judgment and interpersonal skills are likely to remain predominantly human-centric. The framework offers a structured basis for assessing AI's potential impact and may help accounting professionals navigate technological disruption by identifying competencies likely to be important in an increasingly AI-augmented profession.
This paper examines the impact of emerging technologies such as social media and artificial intelligence (AI) on corporate boards' governance function. First, we analyze how boards' responsibilities, governance, and advisory roles evolve as firms incorporate AI into their decision-making processes alongside collective intelligence. Next, we examine how the increased volume and selective filtering of information through social media could intensify disagreements among directors, potentially impairing boards' effectiveness. Finally, we propose potential strategies to address these challenges, enabling firms to harness technological advancements while maintaining board effectiveness.
This case study is built on the collapse of Silicon Valley Bank and requires that students examine and compare financial institutions and assess financial stability. Students are expected to extract public XBRL data, transform that data into informative accounting measures, and load their findings to prepare for evaluation. The case aims to develop students' data analytic skills and accounting intelligence using open-source software and data. In addition to an examination of accounting measures, students will apply textual analysis to evaluate corporate disclosures. This project provides detailed instructions for the use of Python code to extract the XBRL data from the SEC Edgar database and for the use of Excel and/or Python to develop visualizations that communicate their
The proliferation of emerging technologies leads to a change in the accounting education field. To succeed in modern accounting and auditing roles, contemporary accounting professionals must possess data analysis capabilities and proficiency with emerging technologies. This study examines to what extent emerging technologies are currently integrated within educational cases in accounting education literature to prepare accounting graduates for their future job. The integration of emerging technologies in accounting education cases is analyzed through a systematic literature review of 74 case studies published between 2015 and 2025 across seven accounting journals. Each educational case is evaluated based on (1) the emerging technologies incorporated and the accounting discipline area, (2) utilization of unstructured and external data sources, and (3) emphasis on developing students' analytical mindset. Based on the results, actionable recommendations are provided for accounting educators seeking to align their curriculum with evolving professional requirements by integrating emerging technologies into accounting education.
This study examines how students develop professional judgment by analyzing the "Income Taxes" footnotes of Apple Inc. and Amazon.com Inc. The design guides a four-step workflow: (1) compute effective tax rates; (2) identify qualitative drivers: unrecognized tax benefits, deferred tax assets/liabilities/, valuation allowances, and cross-border/legal exposure; (3) apply a deterministic large-language-model prompt using context engineering; and (4) reconcile human and model conclusions to deliver a concise evidence-based comparative conclusion. Structured worksheets and reflections promote reproducibility and professional skepticism. The paper further provides validated reference analyses, additional guidance, and discusses retrieval-augmented generation (RAG) as an optional pathway for source validation and auditability. By linking computational tools with evidence-based reasoning, the study provides a rigorous, ready-to-use framework that accelerates the first-pass reading of dense disclosures while maintaining a central role for human judgment.
This case offers students an opportunity to assume the role of a staff analyst and conduct a qualitative risk assessment using the Delphi Method. We illustrate how the Delphi Method can be used to evaluate the risks associated with adopting and implementing an emerging technology, the Internet of Behaviors (IoB). After completing this exercise, 186 students at a large public university provided feedback regarding the case's efficacy. Their feedback revealed that they found the case interesting and effective for introducing them to qualitative risk assessments and an emerging technology, the Internet of Behaviors.
Higher education institutions (HEIs) have experienced substantial technological advancements since 2020 because of COVID-19 preventive measures and the progress of information and communication technology (ICT). These allowed for investigating new innovative methods of working and learning. As ICT is harnessed by universities, what is the future for online learning? As an extension to online education, this study investigates virtual learning and applies a design-based method to integrate metaverse technology with accounting education. It lays out a model for metaverse technology integration in the accounting curriculum and is poised to change education for students. This "Future Lab" paper proposes a methodology for future applied research, which experiments with metaverse technology in accounting internship courses, providing insights into education in the context of new technology.
Cognitive process automation (CPA) is the process of automating knowledge-intensive tasks that require reasoning, interpretation, and decision-making using artificial intelligence (AI) agentic workflows. Although CPA offers significant potential in audit, auditors often struggle to determine what tasks are suitable for CPA and manage the risks associated with CPA implementation. This study proposes an AI risk reporting tool that operationalizes an AI Risk Assessment Framework for CPA deployment in auditing. Drawing on Task-Technology Fit Theory and Cognitive Load Theory, our framework includes three sequential stages: assessing audit task suitability for CPA, quantifying the cognitive load measure to proxy for AI implementation risk, and translating the measured risk into prescriptive human oversight requirements. This study contributes to the literature by proposing a framework to bridge the current governance gap in CPA deployment and its associated risk.
The Participation and Attendance Tracking Tool (PATT) is designed for university professors to track student participation and attendance in class. With PATT, professors can record both student-and professorinitiated (cold call) participation. The tool allows cold calling to be based on prior participation or random selection. Importantly, PATT will not select students who are not in attendance or those marked by the professor for exclusion from cold calling. Through validation, students expressed a preference for PATT, as it leads to more equitable selection of students, increases student motivation to participate, and helps students better learn and retain material. Additionally, TAs and faculty indicated that they found the tool useful for tracking both student participation and attendance.
Munshi (2024) describes how ChatGPT is transforming and disrupting the audit profession. Although some studies have examined the use of ChatGPT by auditors, little research has focused on its use by clients in financial statement audits. Given the importance of inquiry in the audit process, erroneous or unsupported client responses generated by ChatGPT could lead to inefficiencies and compromise audit quality. We investigate whether ChatGPT 4.0 can provide plausible responses to audit fieldwork inquiries and explore the implications of clients using AI to answer auditor questions. Our preliminary results show ChatGPT can generate plausible responses to basic inquiries and provides guidance for answering more complex questions. This paper highlights the potential for artificial intelligence to disrupt traditional audit procedures and stresses the need for auditors to adapt inquiry methods to mitigate risks associated with AI-generated responses. These findings offer significant implications for practitioners and present new research avenues for academics.
Emerging technologies are not only rapidly changing the accounting and auditing workplace but also how business is done. Technologies, such as robotic process automation, artificial intelligence, and augmented reality, are shifting the skill set required by business professionals, including accountants. Therefore, business curricula need to adapt and incorporate emerging technologies to educate future business professionals in not only understanding the technical features of these technologies but also in how they should be governed, managed, and used responsibly and ethically in business operations. This paper proposes a course on emerging technologies that accounting faculty could deliver to add to any business curriculum, such as accounting, finance, marketing, entrepreneurship, and general business. The course offers a flexible and interdisciplinary framework emphasizing ethics, risk management, and accountability, addressing a gap in existing technology education that primarily focuses on technical proficiency.
The Resource, Events, and Agents (REA) model has been the subject of lively research for over 40 years since it was first developed by McCarthy (1982). That seminal paper has been cited over a thousand times, and the REA model is now the basis for many operational data bases. Our objective in this educational paper is to explain in simple terms what REA is, why it was created, and how it works. We return to the genesis of REA in McCarthy (1979, 1982) and place those papers in the context of accounting and computer technology developments at the time. We then define REA rather than relying on examples, and we explain concepts like duality and ontology that many readers find especially difficult. The audience for this paper is those who recognize the importance of REA but lack access to expert teachers who can help them learn it.
This paper introduces Continuous Artificial Intelligence (AI)-based Reporting, Monitoring, and Assurance (CAIBRMA), extending traditional Continuous Auditing/Continuous Monitoring (CACM) frameworks through AI integration. To provide clarity for future research and practice, we establish distinct boundaries between reporting, monitoring, and assurance functions. By addressing implementation barriers that have limited CACM adoption, AI tools enable us to outline specific integration opportunities across the reporting, monitoring, and assurance functions.
This paper makes the case that the term "hallucinations" is inadequate for describing undesirable results from Generative Artificial Intelligence (GenAI) output. We explain hallucinations, how the term is used, and why it is inadequate. We propose a taxonomy for categorizing these undesirable results, facilitating clearer communication about them. This taxonomy aids in understanding, troubleshooting, and developing resilience against undesirable results, which is important for corporate governance as GenAI integrates both specifically into enterprise resource planning systems, and also software, generally. We give several examples illustrating how our taxonomy provides the necessary language to describe and address undesirable results. We discuss the implications of using the taxonomy widely, particularly in the context of text-to-text Large Language model output, and consider its relevance across different GenAI modalities, as common GenAI multimodal solutions expand.
Trust serves as a foundational factor facilitating efficient and transparent transactions among trading parties. Blockchain has emerged as a platform for enhancing trust and transparency and promises significant improvements in assurance services. Whether trusting a technology influences assurance practitioners' judgment and decision-making is currently unclear. Therefore, it is imperative to understand the perceptions of practitioners and the implications of trusting mechanisms inherent in the network within the context of information assurance. The study examines whether perceptions of practitioners in assurance services about IT risks are influenced by features of information systems based on centralized (e.g., ERP) and decentralized (e.g., blockchain) architectures and how these perceptions shape their IT risk assessment in the assurance process. The findings reveal distinctions in trust perceptions between centralized and decentralized IT architectures and provide valuable insights into perceptions of assurance professionals that may impact assurance strategies, risk evaluations, and resource allocation in the assurance processes.
This study aims to bridge the gap between current auditing standards and regulatory requirements to incorporate emerging technologies into audit procedures. The demand for integrating new technologies into audit procedures, whether explicitly or implicitly, is evident. During the COVID-19 pandemic, auditors faced challenges participating in traditional physical inventory counting, a mandatory procedure according to current auditing standards. Consequently, they inevitably performed remote inventory observation using state-of-the-art technologies. Current auditing standards mandate physical attendance in most cases, yet auditors recognize the potential for remote inventory counting in certain scenarios, albeit subject to specific prerequisites. This study proposes amendments to current auditing standards to facilitate remote inventory observation, enhance audit quality, and mitigate the potential risks associated with adopting this approach. The deployment of robots (i.e., humanoids) in auditing is anticipated in the future. This study serves as a stepping stone to embracing the new technology of physical robots in auditing.
Foundation large language models (LLMs) face limitations in specialized auditing domains, including accuracy issues, contextual memory constraints, and manual document management requirements. This proposal introduces an AI agent framework specifically designed for auditing workflows, integrating three core components: retrieval augmented generation (RAG) for seamless access to private knowledge bases, customizable workflows with intelligent query classification and multiagent coordination, and orchestrated prompts that embed standardized audit methodologies. The proposed framework reduces workflow disruptions and token consumption while maintaining accuracy. The proposal demonstrates the agent's workflow and its capabilities in document retrieval and analytical calculations. The evaluation plans to compare foundation LLM applications with customized AI agents using both baseline RAG and graph RAG approaches across auditing tasks, measuring accuracy against manually generated ground truth and efficiency through time and token consumption metrics.
This paper proposes a solution for the issue of silent shareholders lacking influence over company decisions and not receiving adequate compensation. Thus, we adopt Palmon, Kleinman, and Medinets's (2022) "capital contract" framework and extend it by integrating smart contract functionality. This study then introduces a prototype to demonstrate how this enhanced framework can be implemented through blockchain-based smart contracts. By linking silent shareholders' dividends to executive compensation, these smart contracts enhance the trustworthiness and transparency of the compensation processes for executives and shareholders. What is more, blockchain-based smart contracts automate the contract terms, potentially reducing the need for intermediaries to monitor managerial actions. Also, smart contracts are flexible to meet diverse reporting requirements and adapt to the unique characteristics of a particular company.
This paper discusses the challenges encountered by the audit industry in light of the dearth of welllabeled data and the increasing adoption of machine learning technologies. Although existing machine learning techniques have their merits, they have limitations when it comes to transactional data. Explainable artificial intelligence (XAI) can be a potential solution for applying machine learning models in audit procedures. Primarily, this study discusses challenges related to dependence on preprocessing, verification of explanation, variation in XAI techniques, limitations for feature importance explanation, auditors' attitude to XAI, and computation time. The paper provides some potential solutions for these challenges.
Certified Public Accountants (CPA) exams require candidates to identify cybersecurity risks associated with protecting sensitive and critical information within information systems. This Teaching Note study uses a cybersecurity risk scenario to evaluate accounting students' performance in cybersecurity risk assessment tasks. We ask a sample of 115 graduate accounting students to assess the cybersecurity threats, vulnerabilities, and risks associated with the cybersecurity scenario. As a timely topic, the case exposes accounting students to cybersecurity risk considerations for a specific organization and allows them to investigate cybersecurity concepts (e.g., disgruntled employees) through the lens of internal controls and external auditing. By examining the cybersecurity case, students will gain an understanding of cybersecurity issues faced by organizations and critically think of ways to remediate or implement cybersecurity risk mitigation controls. This Teaching Note could be used in future individual and group exercises in accounting classes.
