<![CDATA[This study critically reviews the extant literature on the relationship between artificial intelligence (AI) adoption and audit quality, with particular attention to the moderating role of auditor competence. A narrative review approach is adopted, synthesizing peer-reviewed studies, professional-body reports, and regulatory publications, primarily published between 2018 and 2026 and drawn from Scopus-indexed and other reputable outlets. The review is anchored on Agency Theory and the Technology Acceptance Model (TAM) to construct an integrative conceptual framework. The reviewed literature indicates that AI can enhance audit quality through expanded data coverage, faster anomaly detection, and more consistent risk assessment, yet these benefits are neither automatic nor uniform. A recurring "audit quality paradox" emerges, in which AI adoption is sometimes accompanied by reduced professional skepticism, overreliance on system outputs, and unresolved explainability concerns. Auditor competence comprising technical knowledge, professional judgment, and digital literacy is identified as a decisive boundary condition that determines whether AI adoption translates into higher or lower audit quality, a condition that is particularly consequential in emerging economies characterized by weaker institutional infrastructure and persistent skills gaps. This paper contributes a theory driven conceptual model that repositions auditor competence as a moderator, rather than a mere control variable, of the AI audit quality relationship, offering a novel and testable research agenda for emerging-market audit contexts.]]>
