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<p data-start="497" data-end="883">In AI grading, NLP begins with text preprocessing, where the system cleans and structures the input by removing unnecessary characters, standardizing spelling, and segmenting sentences. Once the text is processed, the AI uses syntactic analysis to examine grammar, sentence structure, and part-of-speech patterns. This ensures that the writing adheres to basic language rules.

<p data-start="885" data-end="1285">Beyond syntax, semantic analysis allows AI graders to understand meaning and context. This is crucial for evaluating essays, short answers, and open-ended responses. For example, NLP models can detect whether an answer addresses the prompt, stays on topic, and uses relevant evidence. They can also identify paraphrased or plagiarized material by comparing semantic similarity to known sources.

<p data-start="1287" data-end="1577">Advanced NLP-powered AI grading systems also leverage sentiment analysis and coherence modeling to assess tone, flow, and logical progression. This helps in grading subjective assignments such as reflective essays or persuasive writing, where clarity and argument strength matter.

<p data-start="1579" data-end="1909">The benefits of NLP inAI grading include speed, consistency, and scalability. It allows educators to process thousands of responses in seconds while maintaining uniform standards. However, limitations remain&mdash;especially in recognizing nuanced creativity, cultural references, or intentional rule-breaking in writing.

<p data-start="1911" data-end="2208">Ultimately, NLP is not about replacing human graders entirely but augmenting their capabilities. When combined with human judgment, NLP-powered AI grading can deliver fair, efficient, and insightful evaluations, freeing teachers to focus on more personalized feedback and student engagement.