<![CDATA[The rapid advancement of generative AI models such as ChatGPT, Claude, and Gemini raises serious concerns about the authenticity of academic and professional documents. This study develops a detection system that uses a combination of linguistic, structural, and statistical pattern analysis to identify AI-generated text and classify the responsible AI model. The system analyzes more than 12 different parameters from uploaded documents (PDF, DOCX, TXT formats). The detection engine operates through seven analytical layers: signature detection, linguistic analysis, word pattern analysis, structural analysis, feature pattern analysis, vocabulary and grammar assessment, and AI fingerprinting. The scoring mechanism provides a general AI probability score (0-100%) and individual probability scores for 10 different AI models. In testing with 100 documents, the system achieved 76.8% accuracy in identifying AI-generated text and 87.3% accuracy in classifying the source AI model. Sentence entropy analysis, paragraph uniformity assessment, and distinctive linguistic markers proved most effective. This study demonstrates that multi-layer pattern recognition is a viable approach for detecting and classifying AI-generated text, with implications for academic integrity, content verification, and digital forensics.]]>
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