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Generative AI driven Knowledge Reasoner for Simulating Job-Skill Assessment

Principal Supervisor:

Professor Jianxin (Kevin) Li

Associate Supervisor:

Dr Ahmad Khanfar

Abstract

The aim of this project is to address the increasing demand for dynamic reskilling assessment of job applicants in the rapid evolution of workforce market. Traditional matching systems rely heavily on keyword-based parsing or rigid rule-based approaches, which often lack contextual understanding and fail to assess nuanced capabilities. The significance is to innovate a new framework of a Generative AI-driven Knowledge Reasoner (GKR) to simulate human-like assessment of job-skill compatibility by leveraging structured knowledge and generative models. Its impact is to help educators aligning training programs with emerging job skill demanded by workforce expectations, students enabling adaptive course recommendation for their goals, prior knowledge and job market trends, and lifelong learners facilitating modular, micro-credential-based learning tailored to career transitions.

Please refer to PDF for further information.

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