Authors : Dr. Sameer P. Patil

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/58h2ts8p

Scribd : https://tinyurl.com/2cfzmucc

DOI : https://doi.org/10.38124/ijisrt/26May2010

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study explores how Artificial Intelligence (AI) and Machine Learning (ML) can be used to predict which career stream higher secondary students may choose. Many students today find it difficult to choose the right career path. This is because they do not have enough information about different careers. They may also feel confused or unsure about their future. Their choices can also be influenced by family, friends, and other people around them. This study checks whether school marks and personal factors like career awareness, family pressure, peer influence, and self-confidence affect these decisions. Data was collected from 160 higher secondary students using a simple questionnaire. The answers were checked for reliability using a method called Cronbach’s Alpha. After that, the data was studied using basic statistical methods and tests like the Shapiro-Wilk test and the Kruskal-Wallis test. AI methods such as Decision Tree, Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) were used to predict students’ career choices. The results showed that students’ marks and behaviour did not have a strong effect on their career stream choice. The AI models showed different levels of accuracy, but the differences were not statistically important. Among them, SVM gave the best accuracy. The study concludes that career choice depends on many factors. The factors used in this study are not enough to fully explain students’ decisions. Future studies with more data and more factors may give better and clearer results.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Career Stream Prediction, Higher Secondary Students, Academic Performance, Behavioural Factors, Career Choice, Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, Educational Data Mining, Predictive Analytics.

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