An AI-Assisted Framework for European University Interview Preparation | IJCT Volume 13 – Issue 1 | IJCT-V13I1P7

This research presents a comprehensive AI-assisted framework for structured preparation of European university admission interviews. By systematically evaluating interview performance across academic depth, motivation clarity, faculty alignment, communication style, and cultural appropriateness, the study demonstrates that interview outcomes are influenced by multiple qualitative factors that are often under-supported in traditional preparation methods. The experimental results confirm that candidates with strong academic profiles may still underperform in interviews when preparation focuses solely on content knowledge rather than articulation, coherence, and alignment with European academic norms. A key contribution of this work is the development of a unified interview intelligence framework that integrates interview simulation, rubric-based evaluation, and formative feedback into a single system. Rather than treating interview preparation as a static rehearsal task, the proposed platform models faculty-led interview dynamics by generating adaptive follow-up questions and evaluating responses holistically. This approach enables candidates to improve reasoning clarity, anticipate academic scrutiny, and refine their narratives through iterative practice. The study further demonstrates the importance of consistency between written application materials and interview responses. The SOP–interview consistency evaluation module revealed that even minor discrepancies can significantly affect perceived academic credibility. By identifying and addressing these inconsistencies, the system strengthens candidate narratives and reduces vulnerability to probing follow-up questions. This contribution is particularly valuable for postgraduate and doctoral applicants, where coherence of academic intent is a critical evaluation criterion. Cultural communication alignment emerged as another central finding. Baseline interviews frequently exhibited language patterns and self-presentation styles misaligned with European academic expectations. The results show that culturally informed feedback can substantially improve interview performance without altering the substantive content of responses. This highlights that interview disadvantage often arises from communicative mismatch rather than academic inadequacy, underscoring the role of preparation tools in reducing unintentional self-disadvantage. The framework also contributes to transparency in interview preparation by explicitly avoiding deterministic admission predictions. Instead, the admission committee simulator provides qualitative verdicts with academic justification, reinforcing the system’s role as a formative support tool rather than a decision-making authority. This design choice aligns with ethical principles in educational AI by preserving institutional autonomy and candidate agency. Overall, the findings demonstrate that AI-assisted interview preparation can meaningfully enhance interview readiness while maintaining authenticity, fairness, and academic integrity. By offering structured, scalable, and culturally informed preparation, the proposed system addresses a critical gap in European university admissions and contributes to broader efforts to promote equitable access to international higher education.