Fusion of Generative AI with Emotional Intelligence Parameters in Students' Psychometric Career Assessments: An HRM Perspective for Career Management

Abstract

Background Career assessment has traditionally relied on psychometric instruments that evaluate aptitude, personality, interests, and cognitive abilities. However, emotional intelligence (EI) plays a significant role in career decision-making and adaptability. Recent advancements in Generative Artificial Intelligence (GenAI) have enabled the development of personalized career guidance systems capable of integrating multiple psychological dimensions. The fusion of GenAI with emotional intelligence parameters may enhance the accuracy and relevance of career recommendations for students.

Aim: To evaluate the effectiveness of integrating Generative AI with emotional intelligence parameters in students’ psychometric career assessments and to examine its impact on career decision confidence, assessment accuracy, and acceptance of AI-generated career recommendations.

Materials and Methods: A cross-sectional analytical study was conducted among 240 undergraduate and postgraduate students from various academic disciplines. Participants completed standardized psychometric assessments and the Wong and Law Emotional Intelligence Scale (WLEIS). A customized Generative AI framework generated individualized career recommendations based on psychometric and emotional intelligence profiles. Outcomes including career decision confidence, perceived assessment accuracy, recommendation relevance, user satisfaction, and acceptance of AI-generated recommendations were analyzed using Chi-square test, independent sample t-test, ANOVA, and Pearson correlation analysis. Statistical significance was established at p<0.05.

Results: Students with high emotional intelligence demonstrated significantly greater career decision confidence than those with moderate or low EI scores (4.51±0.61 vs. 3.89±0.68 and 3.18±0.72; p<0.001). Acceptance of AI-generated career recommendations increased significantly with emotional intelligence level, ranging from 54.8% among low-EI students to 88.4% among high-EI students (p<0.001). The AI-EI integrated assessment model significantly outperformed conventional psychometric assessment in perceived accuracy (4.42±0.58 vs. 3.56±0.77), recommendation relevance (4.39±0.62 vs. 3.48±0.81), user satisfaction (4.51±0.56 vs. 3.52±0.74), and career self-awareness (4.47±0.53 vs. 3.69±0.69), with all comparisons demonstrating statistical significance (p<0.001).Conclusion: The integration of Generative AI and emotional intelligence parameters significantly enhances psychometric career assessment outcomes. Emotionally informed AI systems provide more accurate, personalized, and meaningful career guidance while improving confidence and acceptance among students. The findings support the adoption of AI-EI integrated frameworks in career management, higher education, and human resource development initiatives.