AspireNextGen: An Explainable AI-Powered Personalized Career and Skill Recommendation System
Abstract
Career decision-making has become increasingly complex due to rapid changes in job roles, evolving skill demands, and the abundance of unstructured information available to learners. Students and early professionals often lack personalized, data-driven guidance that clearly connects their current skills to suitable career paths and actionable learning directions. This paper presents AspireNextGen, an AI-powered personalized career and skills advisor designed to recommend career paths, identify role-specific skill gaps, and generate structured guidance for learners. The system employs a hybrid recommendation approach that combines structured role–skill mapping with semantic similarity analysis of user intent. A controlled natural language generation module translates analytical results into clear, step-wise career guidance. Unlike purely conceptual frameworks, AspireNextGen is implemented as a working prototype with a modular backend, normalized database design, and interactive frontend. The system demonstrates strong potential for explainable, scalable, and deployable career guidance in educational and early professional contexts.
Keywords
Career Recommendation System, Skill Gap Analysis, Explainable AI, Educational Data Mining, Personalized Learning
Citations
IRE Journals:
Garv Danwani, Niyaz Ahmed "AspireNextGen: An Explainable AI-Powered Personalized Career and Skill Recommendation System" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 2175-2180 https://doi.org/10.64388/IREV9I6-1713212
IEEE:
Garv Danwani, Niyaz Ahmed
"AspireNextGen: An Explainable AI-Powered Personalized Career and Skill Recommendation System" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1713212
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