GEN AI BASED INTELLIGENT RESEARCH PAPER VERIFICATION AND AUTHENTICITY ASSESSMENT SYSTEM

Abstract

The Gen AI Based Intelligent Research Paper Verification and Authenticity Assessment System is an Artificial Intelligence-based platform designed to assist researchers, students, reviewers, and academic institutions in evaluating the quality and authenticity of research papers. The increasing availability of online publications and AI-generated content has made it difficult to manually verify every research document. The proposed system uses Generative AI, Natural Language Processing, and information retrieval techniques to support the verification process. The system allows users to upload a research paper in formats such as PDF or DOCX. It extracts important information including the title, authors, abstract, keywords, references, citations, methodology, results, and conclusions. NLP techniques are then used to analyze the document structure, writing patterns, claims, and relationships between different sections. The system can identify missing sections, inconsistent information, citation issues, and potentially unsupported claims. A major feature of the proposed system is source and citation verification. The system can compare references and bibliographic information with available trusted academic sources and identify potential mismatches or incomplete references. It can also analyze whether citations appear relevant to the claims they are associated with. Similarity analysis can be used to identify potentially overlapping content with documents available in the selected reference sources. Generative AI is used to summarize verification findings and present them in an understandable format. The system can generate an authenticity assessment report containing indicators such as citation consistency, structural completeness, content similarity, claim-support observations, and detected anomalies. These indicators are presented as evidence for human review rather than as a final declaration that a paper is authentic or fraudulent. Overall, the proposed system aims to make research-paper verification faster, more systematic, and easier to review. It can assist students, researchers, academic reviewers, journals, and institutions in identifying potential issues before publication or submission. The system should be treated as a decision-support tool because AI-based assessments cannot independently guarantee that a research paper is genuine, scientifically correct, or free from plagiarism. Future enhancements can include integration with academic databases, stronger citation verification, multilingual support, advanced similarity analysis, and expert-review workflows