Abstract: The purpose of this study is to compare the accuracy, level of agreement, and grading speed of artificial intelligence models with those of expert human judgment in authentic assessment contexts. This research is quantitative and quasi-experimental in design. The statistical population consisted of accounting students in the 2025–2026 academic year; the sample was selected via convenience sampling. Data were collected from 40 students. Midterm and final exam scoring were performed by three AI models and two human raters. Data were analyzed using SPSS version 27, and Python code was executed in the Google Colab cloud environment. Descriptive statistics, repeated measures analysis of variance, Cohen’s kappa coefficient, and speed measurements were calculated and evaluated. Preliminary findings indicate that human evaluations are more accurate and stable (higher mean scores and smaller standard deviations). This superiority was consistently supported by a significant interaction between rater type and exam, revealing statistically significant differences in scoring between humans and AI. Agreement between human and AI raters ranged from minimal to moderate, whereas inter-rater agreement among humans was very high. On the other hand, AI models that received feedback demonstrated performance improvements ranging from 10 to 24 times those of human raters without feedback. The findings highlight the role of artificial intelligence in transforming and enhancing the assessment domain, positioning it as a rapid complementary tool in the initial stages of objective scoring or marking, to be used alongside human scoring in order to maintain accuracy and consistency in conceptual accounting examinations.