Artificial intelligence and financial reporting quality: Mapping the intellectual structure, theoretical foundations, and future research agenda

Artificial intelligence (AI) is fundamentally reshaping accounting and financial reporting practices, yet the academic literature on how AI affects financial reporting quality (FRQ) remains fragmented and theoretically underdeveloped. This study provides a comprehensive mapping of this emerging field through a three-layer analytical framework combining bibliometric analysis, natural language processing (NLP)-based construct scoring, and path analysis. Drawing on a systematically curated corpus of 311 peer-reviewed articles published between 2010 and 2026 in Scopus and Web of Science, we document a remarkable annual growth rate of 30.41% in scientific production, identify the intellectual and thematic structures of the field, and provide one of the first large-scale NLP-based assessments of the conceptual distance between AI and FRQ in the literature. A critical finding is that AI and FRQ remain empirically orthogonal at the article level (r = -0.038, p = 0.509), confirming that these two research streams evolve largely in parallel rather than in convergence. We discuss the theoretical implications of this gap and propose a structured future research agenda organised around six priority directions. This study contributes to the accounting information systems and financial reporting literatures by providing a methodologically rigorous foundation for cumulative knowledge-building at the AI-FRQ nexus.