A Comparative Study of AI Models and AI-Based Approaches for Evaluating Subjective Answers in Exams

Education has long relied on manual evaluation methods, but as assessments scale, traditional grading faces challenges of inconsistency, bias, and inefficiency. AI-driven approaches have emerged as promising alternatives, leveraging NLP models, deep learning architectures, and hybrid techniques to enhance grading accuracy and scalability. This paper presents a comparative study of various AI-based models, including BERT-based frameworks, transformers, and recurrent neural networks, analyzing their strengths, limitations, and applications in automated subjective answer evaluation. Key challenges such as contextual understanding, rubric alignment, and fairness in AI-based grading are discussed. This study aims to provide insights into the evolving landscape of automated grading and its potential to transform educational assessments.