Design and Implementation of a Cloud- and AI-Based Framework for Managing the Assessment Cycle and Talent Development in the Electric Vehicle Industry

Abstract

The rapid growth of the electric vehicle (EV) industry creates an urgent need for scalable, data-driven talent management systems capable of aligning workforce competencies with evolving technological demands. This study proposes and implements a cloud- and artificial intelligence (AI)-based framework for managing the full assessment cycle and talent development processes in EV-related organizations. The framework integrates psychometric and technical assessments, competency profiling, learning path recommendations, and performance analytics into a unified digital platform. Using a design-science approach, the research specifies requirements with industry stakeholders, designs an end-to-end system architecture, and evaluates its feasibility and perceived usefulness through expert review and pilot deployment. Initial findings indicate that the framework enhances visibility of talent pipelines, supports more objective and continuous assessment, and enables personalized upskilling strategies that are better aligned with EV industry competency standards. The proposed framework offers a replicable model for other advanced manufacturing and green technology sectors seeking to accelerate workforce readiness through cloud-native and AI-enabled solutions.