Volume no :
9 |
Issue no :
1
Article Type :
Research
Author :
Dr N Mookhambika, M.E, Ph.D., Abdul Kalam N, Dhinesh D, Jagan A
Published Date :
07/2025
Publisher :
kiwi Publications
Page No: 1 - 10
Abstract : Career guidance plays a vital role in helping students make informed decisions by aligning their academic strengths, personal interests, and long-term aspirations with appropriate career paths. However, traditional guidance systems often fall short due to their lack of transparency, limited personalization, and inability to address diverse socio-economic or individual factors. These limitations can lead to biased, generic, or misleading recommendations, negatively impacting students’ futures. To overcome these challenges, this project proposes the development of an Explainable Machine Learning (XML) model based on Decision Tree algorithms to provide personalized and transparent career guidance for higher secondary students. The proposed system integrates multiple data inputs such as academic performance, aptitude scores, personal interests, technical and soft skills, and extracurricular activities. By leveraging the interpretability of Decision Trees, the system presents clear, step-by-step reasoning behind each recommendation. This explainability ensures that students, parents, and educators can understand and trust the model’s outputs. Furthermore, the XML framework includes mechanisms to detect and mitigate biases, ensuring fair and equitable career suggestions regardless of a student's background, gender, or location.By combining explainable AI with user-friendly decision logic, the model enhances user trust, engagement, and acceptance. It not only assists students in identifying suitable career opportunities but also empowers them to understand why those paths are recommended. Overall, this XMLbased career guidance system offers a smarter, more inclusive, and transparent solution for future-ready career planning and decision-making, making it an effective tool for modern educational support systems.
Keyword Career Guidance, Higher Secondary Students, Artificial Intelligence (AI), Explainable Machine Learning (XML), Personalized Recommendations Student Interests, Aspirations Alignment, Decision-Making Support, Stress Reduction
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