Abstract :

The Rapid Development Of Artificial Intelligence (AI) And Natural Language Generation (NLG) Models, Such As GPT And Other Large Language Models, Has Led To A Surge In AI-generated Textual Content Across Social Media, Academic Writing, And Online Publications. While These Technologies Enable Automated Content Creation, They Also Pose Challenges Related To Authenticity, Plagiarism, And Misinformation. This Project Focuses On Detecting AI-generated Texts By Analyzing Linguistic Patterns, Syntactic Structures, And Semantic Features That Differentiate Machine-written Text From Human-authored Content. Methods Such As Stylometric Analysis, Statistical Modeling, Machine Learning Classifiers, And Deep Learning Techniques Are Employed To Identify Characteristics Of AIgenerated Text, Including Repetitive Phrasing, Unnatural Coherence, And Lack Of Personal Context. The Proposed System Provides An Automated And Scalable Solution To Identify AI-written Content, Supporting Educators, Publishers, And Online Platforms In Ensuring Content Integrity, Originality, And Reliability. Keywords: AI-Generated Text Detection, Natural Language Processing (NLP), Stylometric Analysis, Machine Learning, Deep Learning, Text Authenticity, Content Integrity.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

147-151

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite