<![CDATA[The rapid integration of artificial intelligence (AI) into higher education has transformed traditional approaches to teaching, learning, and assessment. Among the essential academic competencies required for university students, academic reading plays a fundamental role in developing critical thinking, information processing, and knowledge construction skills. However, conventional assessment methods often fail to provide comprehensive and individualized evaluations of students’ academic reading strategies. This study explores AI-based mechanisms for assessing academic reading strategies in higher education institutions. The article examines the theoretical foundations of academic reading assessment, analyzes the capabilities of AI technologies in monitoring students’ reading behavior, and identifies key mechanisms that support adaptive assessment processes. The findings indicate that AI-powered assessment systems can provide real-time feedback, track cognitive and metacognitive reading strategies, generate personalized recommendations, and facilitate data-driven instructional decisions. Furthermore, AI-based assessment contributes to improving learner autonomy, reading comprehension, and academic performance. The study concludes that the effective integration of artificial intelligence into academic reading assessment can enhance the quality and objectivity of educational evaluation while supporting personalized learning environments in higher education.]]>
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