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This research proposal explores how citizen-centered learning and career advancement can benefit from artificial intelligence, occupational classification frameworks, and the concept of proactive services. In the current literature, there are a lot of machine learning methods used in various job and training recommendation systems to tackle scientific or real-life problems. However, only a few use the existing occupational classifications to classify job and training advertisements or enable cross-regional labor mobility. Additionally, the quality of public e-services regarding labour market services in each European country varies greatly. For example, even though Estonia is typically referred to as being at the forefront of public service digitization and automation, it has not implemented machine learning methods to match job offers or training with candidates. The matching process between vacancy and job seeker is currently carried out with outdated International Standard Classification of Occupations (ISCO) codes, altered to the Estonian Unemployment Insurance Fund's needs. The ISCO code is assigned to each job vacancy by a company and job wish by a citizen manually. Such functionality is rigid and requires the users to define an accurate ISCO code. Even when the filled-in CV consists of detailed previous work experience and educational background, if the ISCO code is not accurate, the e-service will not help the citizen find a new job. Moreover, in today's public employment service portals, there is typically no option to insert specific skills that a citizen has to receive an increased number of accurate job or training recommendations. Consequently, despite many well-established frameworks dealing with competencies and occupations, citizen-centered public services supporting upskilling and finding a new job are inefficient. In this paper, we put forth a research roadmap for investigating how to enable a technical ecosystem using occupational classification frameworks so that citizens, both employed and unemployed, can receive proactive recommendations about upcoming training events and job vacancies. Such a system should be tailored to support citizen life events. For example, it could consider citizens' previous work and educational background to help with retraining, upskilling, or changing one's career path. Therefore we have initiated a project in collaboration with the Estonian Unemployment Insurance Fund, the Estonian Qualifications Authority, other Estonian public organizations, and partner universities in Latvia and Finland. The research is planned as an action design research to design an artificial intelligence-enabled Virtual Competence Assistant (VCA) for the EU labour market. [ABSTRACT FROM AUTHOR]