| Abstract |
Essay evaluation is a time-consuming and resource intensive procedure that frequently results in inconsistent feedback and delays. By providing a scalable, economical, and effective substitute for human based grading, WriteRight, an AI powered essay evaluation system seeks to expedite this procedure. WriteRight guarantees structured and organized evaluations by utilizing cut edge natural language processing (NLP) and machine learning models, which enhances student learning outcomes and grading uniformity.
The subjective aspect of assessing writing quality makes the essay reading process extremely complicated. Simultaneous evaluation of elements like coherence to academic standards is required. It is challenging to ensure justice among various assessors and institutions when using human based grading since it is prone to prejudice, weariness, and inconsistency. Although there are automated grading systems, they frequently do not offer insightful feedback and are not flexible enough to adjust to various grading rubrics. WriteRight overcomes these constraints by combing NLP and ML models to provide students with thorough, actionable feedback along with structured rubric-based assessment.
The system uses a wide range of AI models, including third-party services like ChatGPT, Deep Seek and Gemini as well as optimized large language models (LLMs) housed on a secure private cloud. Because of this adaptable approach, educational instructors can choose a model that best suits their needs whether this means prioritizing interactive feedback for student engagement or data privacy for extensive standardized testing. WriteRight assesses important writing components such as coherence, argument strength, structure, linguistic ability, and conformity to academic norms. It also offers thorough comments to help pupils improve their writing abilities. WriteRight helps teachers by cutting down grading time, improving accuracy and standardizing assessments through its applications in high schools, colleges, and online learning environments. This solution closes the communication gap between students and teachers by combining automation, transparency, and tailored feedback, improving accessibility, dependability, and insight of essay evaluation.
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