Confidence in generative artificial intelligence for enhancing employability: Evidence from higher education

Abstract

With the growing integration of generative AI (GenAI) into university career development counselling and employment guidance, understanding its psychological impact on graduates has become increasingly important. This study examines how multidimensional confidence developed through GenAI use influences graduates’ perceived employability, aiming to strengthen both theory and practice in AI-supported career preparation. Drawing on decision intelligence theory, the research employs partial least squares structural equation modelling (PLS-SEM) to analyze the roles of perceived usefulness and perceived ease of use as underlying mechanisms. The findings show that three forms of confidence – value-related confidence, human-like interaction confidence, and tech-related affective confidence in GenAI – significantly and positively affect graduates’ perceived employability. Although the model demonstrates strong explanatory power, the mediating effects of perceived usefulness and ease of use across multiple pathways were not statistically significant. Overall, the study highlights that GenAI supports employability not only through technical features, but by shaping graduates’ multidimensional confidence. These findings offer valuable theoretical insights and practical guidance for universities adopting human–AI collaborative approaches to career development and employment services.