An Intelligent Interview Simulation Platform With Automated Assessment and Adaptive Feedback For It Students

This article presents CCInterview, an artificial intelligence–based platform for simulating job interviews with Information Technology students. The system incorporates adaptive question generation, automated assessment, and immediate personalized feedback. Validation was carried out with 42 participants, of whom 20 completed at least three interviews for longitudinal analysis. Statistically significant differences were identified in technical skills across repeated measurements (F = 4.02; p = 0.031), with improvement observed between the first and third interviews (p = 0.024). No significant differences were found in soft skills (p > 0.05), although a positive trend was observed in scores. The perception survey reported mean scores of 4.05/5 (SD = 0.70) for the usefulness of feedback and 3.98/5 (SD = 0.81) for clarity, with more than 75\% of the ratings being favorable. Time sufficiency obtained a mean of 3.71/5 (SD = 0.97). The overall usability score was 63.21 (SD = 15.0) on the SUS scale, placing the system at an acceptable level. Together, the results suggest that iterative practice with adaptive feedback helps improve technical performance in simulated scenarios.