AI-Powered Women’s Entrepreneurship Ecosystems: A Review of Personalized Learning

Abstract ―​

Career guidance has traditionally relied on fixed assessments and generalized advice. Artificial Intelligence (AI) is gradually changing this, making guidance more personalized and more dependent on data. Recent studies have looked at career recommendation, conversational assistance, mentoring, adaptive learning, skill-gap analysis, explainable recommendation, and multilingual support. These lines of work differ widely in their goals, methods, target users, datasets, and evaluation practices. This paper reviews eight selected studies that together cover these developments. It considers how large language models, machine learning, semantic similarity, interest-based profiling, adaptive assessment, knowledge graphs, and explainable AI are applied in career-development settings. The literature shows personalization drawing increasingly on skills, interests, education, career intent, performance, and interaction history. Skill-gap analysis links recommendations to learning pathways, conversational AI makes guidance more natural, and explainability is becoming a requirement for user trust. Several challenges remain: career recommendation and skill development are still treated separately, personalization is rarely continuous, evaluation methods are inconsistent, and few systems bring multiple guidance functions together. The paper closes by outlining research directions in adaptive personalization, explainable recommendation, integrated learning pathways, multilingual interaction, feedback-driven design, and real-time career information.