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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.

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

  • 会议论文

  • 发表日期：

    2025年

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

摘要

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.