AI-Driven CASGN Framework for Evaluating Descriptive Answers in Online Education

There is an enormous demand for automated assessment systems, which can handle the bulk assessments efficiently and effectively. While existing systems perform well in grading objective questions, they break down with subjective or descriptive answers that require a deeper analysis of context and understanding. To address this challenge, we have proposed the AI-Enhanced Context-Aware Semantic Grading Network-state-of-the-art algorithm for instant auto-descriptive response scoring in e-assessments. The AI-CASGN utilizes state-of-the-art AI technologies, particularly NLP and deep learning models-very specifically transformer architectures-during context-based semantic analysis. Beyond mere keyword matching, the system scores what students say and how relevant it is. This takes the concept of grading to an even more fine-tuned art. Scores change with depth and accuracy in the answer given to show complete evaluation. With the dynamic grading framework, adaptation on scores both regarding the accuracy and depth of the answer for a precise evaluation is possible. Inbuilt adaptive learning within the system performance evolves day by day by learning from the new data. AI-CASGN is designed to handle biases in mitigation by considering that there may be different patterns for a response to ensure that responses with varied wording but conceptually correct are fairly graded. It will have to deal with the volumes at scale, hence it is envisaged to process large volumes of responses in minimum time and with high accuracy to be deployed in real time in any educational institution. In future, the system will also provide personalized feedback to students for the purpose of continuous learning and improvement. This represents a scalable, real-time, unbiased tool that assesses subjective answers in modern online learning environments.