Beyond Qualifications Frameworks: Large Language Models and the Future of Global Skills Recognition

Global skills recognition has evolved through decades of innovation, spanning national and regional qualifications frameworks, international conventions, and more recently, digital credentials and artificial intelligence (AI)-assisted recognition systems. This paper brings together the insights of an international working group of researchers and practitioners who examined how AI, particularly large language models (LLMs), can enhance transparency, comparability, and equity in the global recognition of qualifications. The discussion explores how AI might assist in levelling frameworks and enabling job-matching systems that support mobility for diverse learners, including migrants and refugees, while emphasising the continuing need for human oversight, ethical governance, and contextual understanding. By situating these insights within the global discourse on the future of global skills recognition, the paper argues that the next era of recognition systems will depend on the co-evolution of humans and machines—combining computational pattern-detection and human oversight, contextual interpretation, and accountable governance to improve portability, comparability, and fairness, particularly for mobile learners, migrants, and refugees.