This study investigates the potential of electroencephalography-based neurometrics to enhance vocational interest assessment within John L. Holland’s RIASEC framework, which classifies occupational preferences across six dimensions: Realistic, Investigative, Artistic, Social, Enterprising, and Conventional. The research compares occupational interest profiles obtained through a widely used self-report instrument with profiles derived from EEG-based responses recorded while participants engaged with the same assessment items. By integrating neurophysiological measures into vocational profiling, the study explores whether neurometric indicators can provide additional insight into underlying occupational preferences beyond consciously reported interests. The ONET Mini Interest Profiler was administered to 33 participants while EEG data were simultaneously recorded, generating 990 paired observations for comparison between self-reported and neurometric-derived scores. The findings reveal a strong overall association between the two assessment approaches, suggesting substantial convergence between declared interests and EEG-based indicators. However, the analysis also identifies meaningful discrepancies in individual RIASEC profiles. These differences frequently led to distinct occupational recommendations, underscoring the practical significance of even small variations in vocational interest assessment, particularly given the extensive mapping of RIASEC profiles to more than 900 occupations in the ONET system. The results suggest that EEG-based neurometrics may complement traditional self-report measures by capturing less explicit or less consciously articulated dimensions of vocational preference. In a context marked by rapid labour market transformation and increasingly diverse educational pathways, more refined methods for identifying individual occupational interests may improve career guidance and better align personal preferences, educational choices, and professional trajectories. The study also discusses implications for educational institutions seeking to optimise program offerings in support of sustainable education and proposes directions for future research on integrating neurophysiological methods into vocational assessment.