<![CDATA[The misalignment between competencies and career choices among Generation Z necessitates the development of recommendation systems capable of comprehensively accommodating personal preferences. This research implements a Quantum-Inspired Evolutionary Algorithm (QEA) within a web-based career recommendation system using the prototyping model to generate adaptive personalization for multidimensional user profiles. The system integrates qubit rotation mechanisms with an adaptive angle of 0.12 radians through twenty iterations to evaluate compatibility between job attributes and user preferences encompassing work-life balance, learning programs, flexible hours, and mentorship availability. Black-box testing of seven functional requirements demonstrates the system's success in generating ranked recommendations based on personal scores with high sensitivity to preference variations. Quantitative evaluation involving thirteen Generation Z respondents yielded an average score of 4.45 on a five-point scale for the recommendation suitability dimension, confirming the effectiveness of the QEA approach in producing outputs responsive to individual user characteristics.]]>
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