<![CDATA[Abstracting, Documenting, and Archiving Network (ADAN) system was developed to address the challenges of scientific information management in the digital era by integrating abstraction, documentation, and archiving functions in one integrated platform. This study aims to design an efficient and responsive system to academic needs by utilizing artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) technologies. In-depth literature study and needs analysis through interviews, Focus Group Discussions (FGD), and surveys of 250 academic respondents identified priorities such as fast access, data preservation, and content recommendations. ADAN is designed with a modular 5-tier architecture, supporting interoperability with standards such as Dublin Core and OAI-PMH. Test results show that automatic metadata extraction achieves 93% accuracy, 89% content classification, and 0.8 seconds search response time for 10,000 documents. The system also offers an intuitive user interface with a System Usability Scale (SUS) score of 82.5, confirming its potential as an innovative solution in scientific information management.]]>
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