Context-Aware Detection of AI-Generated Academic Submissions Using Behavioral and Linguistic Analysis

Author(s)	Mr. Vysakh K V
Country	India
Abstract	Artificial Intelligence (AI)-based text generation tools are increasingly used by students to produce academic content, raising serious concerns regarding originality and academic integrity. Conventional plagiarism detection systems are not effective in identifying AI-generated text, as such content is typically unique and does not match existing sources. This study proposes a context-aware framework for detecting AI-generated academic submissions by combining linguistic and behavioral analysis. Linguistic features such as vocabulary usage, sentence structure, and repetition patterns are analyzed alongside behavioral features including individual writing consistency and submission characteristics. A machine learning model is developed to classify academic submissions as either human-written or AI-generated. The results demonstrate that integrating behavioral features significantly improves detection accuracy compared to traditional text-based approaches. The proposed system provides an effective and scalable solution for educational institutions to address challenges associated with AI-assisted academic writing.
Keywords	Artificial Intelligence, Academic Integrity, Machine Learning, Text Classification, Behavioral Analysis, AI Detection
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-25

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