International Journal For Multidisciplinary Research

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Integrating Explainable AI and Machine Learning with Dermatoglyphics-Based Intelligence Analysis for Enhanced Career Counseling in Educational Systems

Author(s) Ms. Anamika R. Singh, Kajal Patel, Nidhi Bhavsar, Kriti Das, Dr. Avinash Shivas
Country India
Abstract The integration of XAI with ML will yield a powerful framework for career counseling with data insights. This proposed methodology integrates education data with Dermatoglyphics Multiple Intelligence Test data for deeper insights into the cognitive strengths and preferences of students. Classification of these datasets involves the use of ML algorithms such as Naïve Bayes, Logistic Regression, and Decision Trees. The study emphasizes the performance of the classifiers in terms of their Recall and F-Measure, with Naïve Bayes turning out to be the best. The framework also leverages the power of XAI tools for improved model interpretability, where the decision-making process is made transparent and understandable, hence always adaptable to real-world applications for career guidance.
It further addresses certain key challenges in educational data mining: dataset limitations and the need for adaptable models. The framework is intended to improve the accuracy of career predictions, using XAI to make the results interpretable by students and counselors alike. Precisely, this approach enables personalized recommendations with the use of ML techniques on DMIT patterns to offer students appropriate career suggestions according to their inherent abilities. The novelty here lies in striking a balance between the use of advanced technologies like ML and XAI with trust and transparency requirements for ensuring the reliability and ethical soundness of the whole process of career counseling.
Keywords XAI, DMIT, ML, Educational Data Mining, Career Counseling, Personalized Career Recommendations.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62124
Short DOI https://doi.org/hbdsm6
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