This position paper presents a novel perspective on addressing the challenges of digital transformation in higher education through the development of a qualification-based learning model (QBLM) qualification management methodology. It argues that the rapid pace of technological advancement and the resulting need for continuous upskilling and reskilling necessitate a more dynamic and adaptive approach to human-resource management and development. The paper posits that by extending QBLM through the integration of artificial intelligence (AI) and machine learning (ML), a more effective system for analyzing competence requirements and designing personalized learning pathways can be created. The paper proposes a three-fold approach: (1) developing the FPHR ontology to support semantic annotation of HR qualifications in higher-education institutions (HEIs), (2) integrating this ontology into QBLM to ensure the machine-readability of qualifications, and (3) modeling a knowledge-based production process for HRs in skills-based learning. This paper outlines the current state of the art, presents conceptual models, and describes planned proof-of-concept implementations and evaluations. It contends that this approach will significantly enhance the effectiveness of human-resource development in the rapidly evolving digital knowledge society. By presenting this position, the paper aims to stimulate discussion and collaboration within the academic community on innovative approaches to qualification management in higher education. The work addresses critical issues arising from technological development and offers a forward-thinking solution to bridge the gap between current and future skill requirements in industry and academia.

Continuous learning is fundamental to professional and personal growth. Therefore, robust digital solutions are required to support adaptable and scalable educational demands. The Qualifications-Based Learning Model (QBLM) is a framework for qualifications-based learning, defining a software architecture and data models. However, the existing implementations of QBLM lack horizontal scalability and flexibility, which results in tightly coupled components that limit the adaptability of the model to the evolving needs of learners and institutions. Therefore, a new Qualifications-Based Learning Platform (QBLM Platform) is planned, which extends the QBLM approach by utilizing a modular software architecture that enables flexible service integration, scalability, and operational resilience. However, to design such a QBLM Platform, a requirements analysis is necessary. By employing both quantitative and qualitative research methods, which include a survey and expert interviews, the requirements for a QBLM Platform are identified. The result of the research is used to define not only the essential features and characteristics of the QBLM Platform but also learning platforms in general.