Bridging Skill Gaps: Combining Generative and Symbolic AI for Personalized Lifelong Learning Pathways

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Cédric Pruski, Célia da Costa Pereira, Marcos Da Silveira, Gabriele Marconi, Marie Gallais, Andrea Tettamanzi

Published: 2025, Last Modified: 21 Jan 2026AIED (6) 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In today’s evolving job market, lifelong learning is essential for maintaining competitiveness. Workers must adapt to changing roles by identifying skill gaps and finding relevant training, yet this remains a challenge. This paper introduces a novel two-step approach combining generative and symbolic AI to support personalized lifelong learning. Large Language Models (LLMs) annotate résumés, job descriptions, and training offers with standardized vocabulary. A logic-based framework then detects missing skills and recommends appropriate training. Validated through real-world data, this method effectively supports skill gap analysis and tailored learning strategies.