A Comparative Study of AI Models and AI-Based Approaches for Evaluating Subjective Answers in Exams
Year: 2025
Type: article
Abstract: 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.
Author: Raihana Rasaldeen, Stefi Marshal Fernandes, Irin Rose Jaison, , Ria Mariam Mathews, Fabeela Ali Rawther
Institution: Amal Jyothi College of Engineering
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6949506199
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