This paper presents a comprehensive review of competency frameworks and Artificial Intelligence (AI)-driven Natural Language Processing (NLP) techniques which are essential for an effective skill extraction, mapping, and recommendation in digital skills platforms. With the rapid evolution of labour markets, accurately identifying and aligning individual skills to job requirements is vital. Competency frameworks, such as the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and the Méthodes Informatiques Appliquées à la Gestion des Entreprises (MIAGE) Network Competency Repository, offer structured and standardised skill definitions that bridge gaps between educational outcomes, industry demands, and individual career objectives. This review paper critically analyses how these frameworks can be used to clearly facilitate skill definitions and promote interoperability across diverse contexts.

Further, the paper systematically evaluates advanced AI-enhanced NLP methodologies for automated skill extraction from textual data, including job postings, resumes, and educational materials. Techniques explored encompass transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa), Large Language Model Meta AI (LLaMA), and Generative Pre-trained Transformer (GPT-4.0), detailing their capabilities, effectiveness, and challenges in accurately recognising and categorising skills. In addition, the paper delves into methodologies for skill mapping and the creation of intelligent recommendation systems designed to deliver personalised career guidance and adaptive learning paths.

By analysing existing literature in the field, highlighting strengths, identifying gaps, and combining best practices, this review provides essential insights for developing the Interactive Digital Skills Platform (ReSHAPE). The paper aims to guide future efforts in optimizing competency framework integration and NLP-driven personalisation to enhance skill development, employability, and economic sustainability.

Keywords: Competency Frameworks, Professional development, Lifelong learning, Career paths, Employability, Continuing education, Natural Language Processing.