A Quantum-Optimized Decentralized Ledger for Real-Time TVET Skill Assessment via Wearable IoT Sensors: A Framework for the Kenyan Technical and Vocational Education and Training Sector

There exists an ongoing problem among technical and vocational education and training institutions in Kenya regarding validation of demonstrated practical competencies, since the verification method depends on periodic observation which is paper-based and hence prone to subjectivity and potential corruption. In this article, we propose a quantum-optimized decentralized ledger system in which trainees wear wearable Internet of Things sensors and a consensus algorithm referred to as Quantum-Optimized Decentralized Proof-of-Skill is used to record and validate skill demonstration. The sensors detect motion, physiological, and environmental signals that are converted into skill proficiency ratings using an edge-based feature-processing pipeline; validator nodes are then chosen by solving a quadratic unconstrained binary optimization problem with a quantum-inspired algorithm. Assessment records are written to a permissioned ledger accessible only to training institutions, national qualification authorities, and prospective employers. According to a discrete-event simulation of the proposed consensus algorithm relative to proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, the proposed algorithm has a reduced mean confirmation time of 148 ms and an increased sustained throughput of 386 tx/s under the modeled conditions of the Kenyan network environment, alongside skill-scoring consensus (F1 overall = 0.88) with the assessor ground truth obtained through simulations based on the accuracy of the wearable-assessments in published reports.