From experience to evidence: a human–AI conceptual model for assessing and credentialing lived learning

Lived experience is a powerful medium for learning, yet most educational and workforce systems lack tools to recognize complex competencies developed in homes, communities, and workplaces. This paper introduces LivedX, a socio-technical platform designed to make lived learning visible, valid, and portable across education and employment. LivedX integrates three core components: an integrative taxonomy of human capacities aligned with NACE; a human–AI annotation pipeline for coding narratives of lived experience; and a digital platform that translates narrative evidence into interpretable profiles and credential-ready artifacts. Drawing on iterative conceptual work and large-scale human coding of learning narratives, we describe how the LivedX taxonomy was developed by mapping existing frameworks, building a hierarchical codebook, and refining it through consensus-oriented annotation practice. The system was empirically evaluated on a corpus of approximately 3,000 learner narratives, achieving strong inter-rater reliability (ICC = 0.83) and improved automated competency prediction performance, including a 12% increase in F1 score and a 5% increase in AUROC relative to baseline models. We outline a workflow in which learners submit narratives, a natural language processing engine proposes competency mappings with embedded explanations, and human reviewers retain final judgment. LivedX offers a scalable model for equity-oriented recognition of non-formal learning.