AI-Powered Credential Intelligence and Degree Discovery Frameworks for Academic Pathway Analysis
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P118Keywords:
Academic Pathway Analysis, Credential Intelligence, Degree Discovery, Artificial Intelligence, Knowledge Graphs, Recommendation Systems, Higher Education AnalyticsAbstract
The rapid expansion of digital credentials, interdisciplinary degree programs, and alternative learning pathways has created unprecedented complexity in academic planning and workforce alignment. Traditional academic advising systems rely heavily on static curricula, manual interpretation of transcripts, and limited labor-market intelligence, which constrains their ability to provide personalized, future-oriented guidance. This paper proposes an AI-Powered Credential Intelligence and Degree Discovery Framework (AICIDDF) designed to analyze, interpret, and recommend academic pathways using advanced artificial intelligence techniques. The framework integrates natural language processing (NLP), knowledge graphs, machine learning-based recommendation systems, and predictive analytics to extract semantic meaning from heterogeneous credential data, including degrees, micro-credentials, certifications, and experiential learning records. The proposed approach introduces a unified credential ontology that enables cross-institutional degree discovery and comparability. By modeling relationships between skills, courses, credentials, and occupational outcomes, the framework supports intelligent pathway analysis that adapts to individual learner profiles and evolving labor-market demands. The methodology encompasses data ingestion, credential normalization, feature engineering, AI-driven inference, and explainable recommendation mechanisms. Experimental evaluation using simulated multi-institutional datasets demonstrates improved accuracy in pathway recommendation, enhanced transparency in decision support, and scalability for large academic ecosystems. The findings indicate that AI-powered credential intelligence can significantly enhance academic advising, institutional planning, and learner employability. This work contributes a comprehensive architectural model, analytical methods, and evaluation metrics aligned with 2025-era digital education systems. The proposed framework lays the foundation for interoperable, ethical, and adaptive academic pathway intelligence systems suitable for higher education institutions, accreditation bodies, and lifelong learning platforms
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