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Design and Implementation Concept of an AI-Powered Scholarly Discovery Platform for Emerging Research Ecosystems
Authors:
| Md. Hafizur Rahman | HafizLab, Bangladesh |
| Muhammad Shihab | HafizLab |
| M. Naderuzzaman | Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh |
Submission Date: 01-10-2025, Accepted Date: 01-01-2026, Publication Date: 25-01-2026
https://oajea.hafizlab.com/article/01-02-002 
https://doi.org/10.64886/oajea.0102.002 
Index Terms:
Scholarly discovery, artificial intelligence, semantic search, research ecosystems, metadata enrichment, academic visibility, citation graph, recommendation systemsAbstract:
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.Conclusion:
The emergence of artificial intelligence has opened new frontiers in scholarly communication, enabling automation, semantic understanding, and data-driven insights that were previously unattainable. However, despite these advancements, significant disparities persist in global research visibility—particularly within developing and emerging research ecosystems. This paper addressed these disparities through the conceptual design and implementation framework of an AI-powered Scholarly Discovery Platform (AI-SDP) aimed at improving the accessibility, discoverability, and inclusivity of academic knowledge. The study proposed a five-layer conceptual architecture encompassing data sourcing, ingestion and normalization, intelligence processing, API and application services, and user interaction. Each layer was designed with interoperability, scalability, and transparency in mind. The framework leverages open metadata standards, AI-based natural language processing, and semantic graph technologies to enable hybrid search, automated metadata enrichment, citation graph analysis, and explainable recommendation systems. By aligning with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, the AI-SDP model ensures compliance with open-science values while maintaining institutional autonomy and data sovereignty. A comprehensive review of related literature revealed that existing scholarly discovery systems—such as Scopus, Web of Science, and Google Scholar—are largely limited by closed data models, linguistic bias, and underrepresentation of local journals. In contrast, the AI-SDP framework emphasizes inclusivity through multilingual processing, metadata normalization, and integration with diverse data sources. It bridges the technological and knowledge divide by combining AI-driven intelligence with an open, federated architecture suitable for both global and regional implementations. The proposed system’s evaluation framework defines multiple layers of assessment, including functional accuracy, retrieval performance, and user experience. Conceptual Key Performance Indicators (KPIs) such as metadata completeness, retrieval relevance (Precision@K, nDCG), and visibility improvement metrics were outlined to guide prototype validation. The anticipated outcomes include enhanced research discoverability, improved metadata quality, increased collaboration among institutions, and equitable participation in the global scholarly communication network. From a strategic standpoint, AI-SDP contributes not only as a technological solution but also as a socio-technical model for research capacity building. It supports digital transformation in academia by empowering institutions to develop autonomous, interoperable repositories and by promoting transparent, explainable, and ethical AI practices. Its federated deployment model aligns with emerging open-science policies, ensuring that each institution retains control over its data while contributing to a globally connected infrastructure. Nevertheless, the study acknowledges challenges in areas such as metadata heterogeneity, computational resource limitations, and multilingual model adaptation. These obstacles will be addressed in future work through pilot testing, incremental deployment, and collaborative partnerships with academic institutions and policy bodies. The roadmap for future development includes prototype implementation, real-world usability testing, and integration with global infrastructures such as ORCID, OpenAIRE and OpenCitations. In conclusion, the AI-SDP conceptual framework represents a transformative vision for scholarly communication. By merging artificial intelligence with open standards and ethical governance, it establishes a foundation for an inclusive, transparent, and intelligent research discovery ecosystem. The proposed design empowers emerging research communities to participate actively in global knowledge exchange, ensuring that locally produced research attains rightful visibility and impact. Ultimately, this study demonstrates that the convergence of AI, open data, and federated architectures can democratize access to scholarly knowledge, strengthen institutional collaboration, and foster global equity in research dissemination. The AI-SDP framework thus provides not only a conceptual pathway but also a practical blueprint for the future of digital scholarship in the 21st century.License:
Articles published in OAJEA are licensed under a Creative Commons Attribution 4.0 International License.Cite This Paper:
Md Hafizur Rahman, Muhammad Shihab, M. Naderuzzaman, “Design and Implementation Concept of an AI-Powered Scholarly Discovery Platform for Emerging Research Ecosystems”, Open Access Journal on Engineering Applications (OAJEA), Volume No. 01, Issue No. 02, Page 8-18, January, 2026. https://doi.org/10.64886/oajea.0102.002Reference:
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Journal Information
| Frequency: Quarterly |
| Submission to First Decision: 7 days |
| Submission to Acceptance: 30 days |
| Accept to Publish: 15 days |
| Article Processing Charges (APCs): None |
