ANALYSIS OF EXISTING METHODOLOGIES FOR ASSESSING STUDENTS' KNOWLEDGE AND SKILLS USING ARTIFICIAL INTELLIGENCE ALGORITHMS

This study analyzes contemporary methodologies of Automated Essay Scoring (AES) systems based on artificial intelligence (AI) algorithms and their pedagogical foundations. The research examines the limitations of traditional assessment approaches, including subjectivity, excessive time consumption, and insufficient individualization. The effectiveness of Natural Language Processing (NLP), machine learning, and deep learning technologies integrated into AI-based systems is evaluated. A comparative analysis of the architectural and algorithmic characteristics of e-rater, Criterion, WriteToLearn, and Turnitin Feedback Studio systems is conducted. The capabilities and constraints of these systems are assessed through the lens of psychometric reliability, validity, and fairness principles. Theoretical frameworks and practical recommendations for implementing AI-based assessment in Uzbekistan's higher education system are developed. Results demonstrate that AI systems achieve correlation coefficients of 0.88–0.92 with human raters and reduce assessment time by 40–60 fold.