Stylometric Fingerprinting with Contextual Anomaly Detection for Sentence-Level AI Authorship Detection

The proliferation of AI tools in academic writing poses significant challenges for verifying the authenticity of student submissions, particularly at the sentence level. This paper proposes a novel Stylometric Fingerprinting with Contextual Anomaly Detection approach to distinguish AI-generated sentences from student-authored ones in writing reports. By combining manual stylometric analysis with contextual coherence checks, our method achieves sentence-level granularity without requiring computational tools or LLMs, making it accessible for student use. We compare our approach to existing models like Turnitin, GPTZero, and Moss, highlighting its unique focus on manual, coherence-driven detection. Experimental insights and theoretical analysis demonstrate its feasibility and effectiveness in ensuring academic integrity.