AI-POWERED ADAPTIVE LEARNING & ASSESSMENT: DRIVING PERSONALIZATION IN EDUCATION AND WORKFORCE UPSKILLING

Abstract

Artificial Intelligence (AI) is revolutionizing modern education by enabling adaptive learning systems that personalize instruction and assessment to each learner’s cognitive profile and pace. This paper explores the development and impact of an AI-powered adaptive learning and assessment framework designed to drive personalization in education and workforce upskilling. Using a hybrid methodology that combines learner analytics, machine learning algorithms, and real-time performance tracking, the study investigates how intelligent models dynamically adjust learning content, assessment difficulty, and feedback mechanisms based on individual learner responses. The system employs clustering algorithms for learner profiling, reinforcement learning for adaptive pathways, and predictive modelling for skill-gap analysis. Findings from pilot implementations demonstrate significant improvements in engagement levels, assessment accuracy, and retention rates compared to traditional Learning Management Systems (LMS). Moreover, the framework’s integration into corporate training highlights its role in bridging competency gaps and supporting continuous professional development. The study concludes that AI-driven personalization enhances both learning outcomes and workforce agility, paving the way for scalable, data-informed educational innovation across domains.