This paper explores the theoretical and practical aspects of designing an artificial intelligence-based web platform for automated assessment of students’ independent work assignments in higher education institutions. The main objective of the study is to develop a conceptual model of an assessment system that integrates natural language processing (NLP), machine learning algorithms, and cloud infrastructure to ensure objectivity, scalability, and adaptability of the grading process. The paper analyzes the evolution of automated assessment systems, their role in education, and current technological approaches. Particular attention is given to the architecture of the proposed platform, including frontend, backend, and AI modules, as well as dataset requirements and the functioning principles of evaluation algorithms. The study considers semantic analysis methods based on BERT/GPT models, text similarity detection, plagiarism checking techniques, and real-time analytics tools. A comparative analysis with widely used platforms such as Google Classroom, Coursera, and Moodle is conducted, highlighting the advantages of the proposed system. A key feature of the platform is its adaptation to the Kazakhstani educational context, including support for the Kazakh language. The findings demonstrate that the implementation of such a system can significantly reduce instructors’ workload, improve assessment accuracy, and provide timely feedback to students. The proposed solution represents an important step toward the digitalization and modernization of higher education systems.

Eleubergen ZH. A.

Master's student, K. Zhubanov Aktobe regional university, Aktobe, Kazakhstan.

E-mail: janbolat.work.2025@gmail.com, https://orcid.org/0009-0006-9737-5440

BAIBAKTINA A.T

Candidate of pedagogical sciences, docent, K. Zhubanov Aktobe regional university, Aktobe, Kazakhstan

E-mail: aksaule67@mail.ru, https://orcid.org/0000-0001-7872-1252

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adaptive system, artificial intelligence, automated assessment, cloud technologies, independent work, machine learning, NLP, web platform