Adult learners cumulate various skills during their professional careers, leading to fragmented competency credentials that are often difficult to assess when seeking to complete higher education degrees. Research suggests that Prior Learning Assessment (PLA) credits facilitate graduation 2.5 times more than those who do not receive credit (Boden et al., Prior learning assessment in the US: A systematic literature review. American Association for Adult and Continuing Education. 2021). The Credential-to-Outcome Recognition Ecosystem (CORE) is proposed as an innovative framework that leverages Gen AI to map various credentials through nationally recognized skill taxonomies to accelerate the PLA Process. The CORE framework addresses skill extraction methodologies and credential interoperability challenges through already structured occupation and skill pathways in the European Skills, Competences, Qualifications, and Occupations (ESCO) taxonomy. The CORE framework can parse the professional digital profile of a distinct learner to pattern match against ESCO-identified skills based on their experience and career paths. Then, the AI CORE framework can identify skill gaps that can be addressed via microcredential skill or assessment activity by analyzing the syllabi and course catalog. Prospective students can significantly save costs and time associated with obtaining a specific degree by obtaining only the missing skill gaps via targeted microcredentials or assessments. The proposed framework improves the PLA process while increasing student satisfaction.