An AI-Driven Framework for Inclusive and Personalized Skill Assessment

Abstract: Assessment is a critical component of education, vocational training, and workforce skill development, yet conventional assessment systems continue to depend on fixed question sets, uniform response formats, and manual evaluation. These practices do not adequately account for differences in learner ability, preferred assessment mode, or accessibility requirements. this paper proposes and defines an evaluable AI-driven inclusive assessment framework that couples adaptive question selection, automated evaluation of objective and descriptive responses, accessibility support, skill-gap analysis, and performance analytics within a single platform, while retaining a human reviewer for uncertain or high-impact decisions. The framework supports multiple-choice, descriptive, practical, and viva-based assessment modes for candidates with a range of abilities and accessibility needs.The methodology follows nine interconnected stages, from requirement analysis and candidate modelling through adaptive selection, automated evaluation, accessibility support, skill-gap analysis, analytics, human review, and privacy governance.against which the enhanced pipeline can be compared, and evaluation is organised around measurable, component-wise criteria: question-selection efficiency and skill-estimation consistency for adaptive testing; scoring agreement (e.g., Cohen's kappa) and error analysis for automated evaluation; usability and accessibility-issue counts for the accessibility layer; and response-time and throughput for system performance.; instead, the exact baseline, metrics, and comparison procedure that a subsequent implementation will use are defined precisely. The principal contribution is not any single AI technique but the integration of previously separate capabilities into one accountable, measurable, and inclusive assessment workflow suitable for a diverse skill ecosystem.

Keywords: AI-Driven Assessment, Inclusive Assessment, Adaptive Testing, Automated Evaluation, NLP, Skill-Gap Analysis, Accessibility, Human-in-the-Loop