<![CDATA[This study examines the effect of artificial intelligence (AI) intensity on audit quality among accounting interns at registered public accounting firms (KAP) in Indonesia. The accelerating deployment of AI technologies — including machine learning, robotic process automation (RPA), data analytics platforms, and anomaly-detection algorithms — has fundamentally restructured audit practice; yet the individual-level implications for early-career auditors operating within AI-augmented environments remain empirically underexplored. Anchored in the Technology Acceptance Model (TAM) and Agency Theory, this study adopts a quantitative, cross-sectional design, collecting primary data from 31 purposively sampled accounting interns through a validated five-point Likert-scale questionnaire. Data were analysed using simple ordinary least squares (OLS) regression in SPSS. Descriptive statistics reveal a moderate level of AI intensity (M = 3.23, SD = 0.71) and a moderately high level of perceived audit quality (M = 3.65, SD = 0.69) within the sample. The OLS regression model is statistically significant (F = 9.636, p = 0.004, R² = 0.249), and the AI intensity coefficient is positive and significant (B = 0.485, β = 0.499, t = 3.104, p = 0.004), indicating that each unit increase in AI intensity is associated with a 0.485-unit improvement in perceived audit quality. These results confirm H1 and provide micro-level quantitative evidence that higher AI integration enhances audit outcomes among interns. Concurrently, the study highlights the latent risk of overreliance: uncritical acceptance of AI-generated outputs may erode professional scepticism — a competency that remains irreplaceable in high-stakes financial reporting verification.]]>
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