A CLOUD-BASED AI SYSTEM FOR SKILL GAP ANALYSIS AND TRAINING PATH RECOMMENDATION IN HR DEPARTMENTS
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Date
2025
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Publisher
Saudi Digital Library
Abstract
This dissertation presents the development of a cloud-based artificial intelligence (AI) system designed to automate skill gap analysis and provide personalised training recommendations in Human Resource (HR) departments. The system integrates employee profiles, job role requirements, and training histories to identify competency gaps using a decision tree algorithm. The AI model achieved an accuracy of 0.86 and demonstrated strong interpretability and efficiency in recommending relevant training paths. Usability testing with HR professionals confirmed the system’s practicality and reliability in supporting workforce development and data-driven training strategies. The research contributes to the field of HR analytics by combining Human Capital Theory with Knowledge Discovery in Databases (KDD) to provide an explainable, scalable, and cloud-enabled HR decision-support framework.
Description
This thesis presents the design and implementation of a cloud-based artificial intelligence (AI) system for automating skill gap analysis and training recommendations in Human Resource (HR) departments. It applies a decision tree model to compare employee skills with job requirements, identifying areas for development and suggesting personalised training paths. The system achieved an accuracy of 0.86 and demonstrated strong interpretability and usability among HR professionals, offering a scalable and explainable AI solution for workforce development.
Keywords
Cloud Computing, Artificial Intelligence, Human Resources, Skill Gap Analysis, Decision Tree, Training Recommendation, Machine Learning, HR Analytics
Citation
Alanazi, A. R. D. (2025). A Cloud-Based AI System for Skill Gap Analysis and Training Path Recommendation in HR Departments. De Montfort University.