Artificial Intelligence Approaches for Evaluating Student Performance: A Review of Methods and Trends
TL;DR
The primary conclusion of this paper shows that, in complicated circumstances, DL methodologies regularly dominate classical ML techniques in terms of accurate prediction, and makes it easier to implement AI evaluation instruments in actual academic institutions.
Abstract
In academic assessments, artificial intelligence (AI) is currently a revolutionary framework that makes it achievable to evaluate student performance in a variety of academic contexts in an adaptable and structured manner. AI programs have an extraordinary effect on the learning, success and performance of students. With an emphasis on methodology-based, quantifiable outcomes, and usability in actual academic environments, this study provides a comprehensive and thorough analysis of cutting-edge AI approaches such as Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) published from 2022 to 2026. Additionally, this study evaluates emerging trends like integrated analysis, explainable AI (XAI) and fairness-based models. The primary conclusion of this paper shows that, in complicated circumstances, DL methodologies regularly dominate classical ML techniques in terms of accurate prediction. Moreover, the absence of uniform assessment models, inadequate incorporation of theoretical concepts in education and restricted potential for generalization among databases are among the primary difficulties addressed. This work influences it because it offers a cohesive and thoroughly reviewed benchmark that helps professionals as well as scholars to create accurate, comprehensible and morally sound AI platforms or models. Furthermore, this comprehensive review makes it easier to implement AI evaluation instruments in actual academic institutions.