10.64886/oajea.0102.002

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Abstract: Emerging research ecosystems, particularly within developing regions, continue to face significant challenges in accessing, indexing, and disseminating scholarly knowledge. Existing global discovery platforms, such as Scopus, Web of Science, and Google Scholar, frequently underrepresent locally produced research outputs due to incomplete metadata coverage, limited interoperability, and linguistic barriers. This paper presents a conceptual design and implementation framework for an AI-powered Scholarly Discovery Platform (AI-SDP) aimed at enhancing the visibility, accessibility, and discoverability of academic resources from underrepresented regions. The proposed framework integrates artificial intelligence, natural language processing (NLP), and semantic graph technologies to enable advanced metadata enrichment, hybrid semantic search, citation graph analytics, and personalized recommendation services. The conceptual architecture is organized into five layers—data source, ingestion, intelligence, application, and user interface—each designed for interoperability, scalability, and inclusivity. By adopting open standards such as Dublin Core and Schema.org, the system ensures compatibility with institutional repositories and open-access data sources. Furthermore, the platform promotes transparency, explainable AI, and FAIR (Findable, Accessible, Interoperable, Reusable) data principles to foster equitable participation in global scholarly communication. This conceptual study contributes to the digital transformation of academic discovery infrastructures by providing a sustainable, AI-driven model that bridges the knowledge visibility gap and empowers emerging research communities to participate effectively in the global scientific ecosystem.