In this issue, we present several interesting papers centered around artificial intelligence (AI). Generative AI (GenAI), in particular, has moved rapidly from curiosity to the center of discussion in accounting education and practice. Yet one dimension remains significantly underexamined: the linguistic and cultural limitations embedded within today’s large language models (LLMs). This editorial highlights three emerging risks that deserve the attention of accounting researchers, educators, and policymakers. Recent studies demonstrate that LLMs may encode systematic performance gaps and cultural priors that subtly influence professional judgment—especially in global contexts.

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Tawei (David) Wang, 2025, Preparing Globally‑Minded Accountants in the Age of GenAI: Why Language and Culture Matter More Than We Think, International Journal of Computer Auditing, Vol.7 No.1, Pages 1-2.
https://doi.org/10.53106/256299802025120701001

    This study investigates whether risk factor disclosures in 10-K annual filings contain predictive signals about firms’ future financial performance. Both structured (financial statements) and unstructured (risk factor narratives) data were analyzed using text mining and machine learning techniques. The JCAATs XBRL Connector and AI audit functions were employed to streamline data extraction, sentiment analysis, clustering, machine learning modeling, and SHAP-based interpretability. Sentiment scores and textual clusters were constructed as independent variables to explain subsequent- year net income. Empirical findings demonstrate that both sentiment and textual style are significant predictors of net income, supporting the view that risk disclosures provide forward-looking information. These findings extend language signal theory to the risk factor section and underscore the practical value of JCAATs in auditing and regulatory monitoring, highlighting how AI-driven text analytics can enhance disclosure assessment and financial supervision.

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Sherry Huang | Shi-Ming Huang, 2025, Text Mining and Machine Learning on 10-K Risk Factors and Net Income: Evidence from Apple, International Journal of Computer Auditing, Vol.7 No.2, Pages 3-15.
https://doi.org/10.53106/256299802025120701002

    The growing volume of unstructured textual data in tax ad- ministration—such as transaction descriptions, audit notes, and reconciliation narratives—has reduced the effectiveness of traditional keyword-based analytics, which fail to capture semantic meaning. This study proposes a transformer- based text-mining framework that converts narrative fields into contextual embeddings to identify tax-relevant patterns more accurately. Using a practice-as-research approach integrated with Design Science principles, the study demon- strates how low-code, freeware tools can operationalize transformer models for compliance testing. Results show improved semantic coverage and more complete detection of tax-risk indicators compared with keyword search, while remaining cost-efficient and accessible for tax professionals.

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Agung Darono, 2025, AI-Driven Tax Analytics with Transformer-Based Text Mining, International Journal of Computer Auditing, Vol.7 No.3, Pages 16-31.
https://doi.org/10.53106/256299802025120701003

    This research explores an evolutionary AI-driven auditing system for the public interest that connects agentic AI and stakeholder primacy—two emerging discourses rarely bridged in current accounting and auditing research—and integrates insights from sustainability accounting, AI auditing, and regulatory policy, demonstrating how technology can support public-interest assurance. The integrated reports of a Japanese telecommunications company, which aims for carbon neutrality, and a Japanese electric company, which aims for sustainable development for its local communities, are analyzed. The results show that an AI agent helps a practitioner judge sustainability information from a different perspective, provided by another company’s integrated report.

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Tomita Aki , 2025, Agentic AI: Evaluating Value Creation Stories of Individual Companies From a Stakeholder Primacy Perspective, International Journal of Computer Auditing, Vol.7 No.4, Pages 32-51.
https://doi.org/10.53106/256299802025120701004

    Artificial intelligence (AI) is rapidly transforming the auditing profession (Dong et al. 2023; Gu et al. 2024). Early pplications primarily focused on automating routine tasks, such as journal entry testing or anomaly detection. More recently, advances in AI have given rise to agentic AI—systems capable of autonomous goal setting, iterative reasoning, and adaptive task execution (Li et al. 2025). Unlike traditional decision-support tools, agentic AI can actively participate in audit processes by identifying risks, proposing procedures, and generating documentation. This shift raises fundamental questions regarding auditor responsibility, professional judgment, and accountability. These issues are particularly salient in jurisdictions with strong regulatory oversight and high legal exposure for auditors. The Korean audit market provides a distinctive and informative setting in this regard. Korea is characterized by stringent auditor liability under the External Audit Act, intensive regulatory inspections by the Financial Supervisory Service (FSS), and a growing emphasis on audit process documentation and consistency. This commentary examines how agentic AI may be integrated into auditing through a human–AI collaboration framework, focusing on the roles, benefits, and risks of such collaboration in the Korean audit environment.

Reference Format:
Hongmin Chun , 2025, Agentic AI and Human–AI Collaboration in Auditing: Roles, Benefits, and Risks in the Korean Audit Market, International Journal of Computer Auditing, Vol.7 No.5, Pages 52-56.
https://doi.org/10.53106/256299802025120701005

    Artificial intelligence has been discussed in auditing as a set of tools designed to enhance efficiency, expand analytical capacity, and support auditors\' professional judgment. A substantial body of accounting information systems (AIS) research has emphasized that technological advances reshape audit processes without displacing the central role of professional judgment (e.g., Vasarhelyi et al., 2004; Kokina et al., 2025). Advances in large language models and multi-agent systems, however, mark a departure from this traditional framing. These developments have given rise to agentic AI, which refers to AI systems capable of decomposing objectives, initiating actions, coordinating subtasks, and iteratively refining outputs. As a result, agentic AI raises qualitatively different questions from earlier generations of audit analytics or computer-assisted audit techniques.

Reference Format:
Sheng-Feng Hsieh, 2025, Agentic AI and Human-AI Collaboration in Auditing: Reframing Roles, Accountability, and Governance, International Journal of Computer Auditing, Vol.7 No.6, Pages 57-62.
https://doi.org/10.53106/256299802025120701006