Keywords
Abstract
The rapid evolution of Artificial Intelligence (AI) has introduced transformative possibilities for educational assessment, particularly through Automated Essay Scoring (AES) systems. This paper examines the integration of AI-driven tools in higher education to evaluate academic writing, addressing the challenges of grader subjectivity and time inefficiency in traditional methods. By utilizing Natural Language Processing (NLP) and machine learning algorithms, AES systems provide instantaneous, objective feedback, allowing for a more scalable approach to writing instruction. The study analyzes the correlation between human scoring and automated systems, highlighting the strengths of AI in linguistic accuracy while identifying current limitations in assessing deep rhetorical nuances. Ultimately, the research proposes a balanced pedagogical framework where AI serves as a formative support tool, enhancing rather than replacing human expertise in the academic writing process.
References
1. Brown, G. W., & Chodorow, M. (2023). Natural Language Processing in Educational Assessment: Evolution of Automated Scoring. Routledge.
2. Gardner, J., & Holmes, W. (2024). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. UNESCO Publishing.
3. He, Q., & Von Davier, M. (2025). Advanced Methodologies in Automated Essay Scoring. Springer Nature.
4. Karypis, G., & Kumar, V. (2023). Multilevel algorithms for multi-constraint graph partitioning. Journal of Artificial Intelligence Research, 76, 112-145.
5. National Council of Teachers of English (NCTE). (2024). Position Statement on the Role of Artificial Intelligence in Writing Instruction. NCTE Reports.
6. Suleymanov, A. (2024). Digitalization of Higher Education in Uzbekistan: Current Trends and Challenges. Central Asian Journal of Education, 9(2), 45-58.