AI-Based Automatic Answer Evaluation System

The accelerating digital transformation of educational institutions worldwide has precipitated an unprecedented demand for assessment mechanisms that are not only scalable and efficient but also demonstrably objective and consistent. While the automatic grading of multiple-choice questions has long been a solved problem—owing to the unambiguous nature of their responses—the evaluation of descriptive, short-form answers continues to pose a formidable challenge. Such open-ended responses require a nuanced semantic understanding of language, context, and intent, far exceeding the capabilities of superficial keyword-matching techniques that dominated early approaches. This paper presents a comprehensive survey of the contemporary research landscape in Automated Short Answer Grading (ASAG), tracing the field's evolution from traditional natural language processing methodologies to sophisticated deep learning architectures—including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and transformer-based models such as Sentence-BERT (S-BERT)—as well as emerging secure frameworks designed to safeguard academic integrity. Through a systematic comparative analysis, existing approaches are rigorously evaluated along three critical dimensions: grading accuracy, multilingual capability, and computational efficiency. Our investigation reveals persistent and critical research gaps, most notably the inability of current systems to robustly accommodate handwriting variation and the pervasive lack of explainability inherent in black-box neural models. To bridge these deficiencies, we propose a novel architectural framework that synergistically integrates Gemini Vision AI for multimodal OCR-based ingestion of handwritten scripts, pre-trained sentence embeddings for deep semantic representation, concept-level rubric scoring to mirror human evaluative judgment, and human-in-the-loop audit governance to ensure transparency, accountability, and trust in automated academic assessment.