AN AI-BASED PERSONALIZED INTERVIEW PREPARATION AND CANDIDATE EVALUATION SYSTEM USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING

Artificial Intelligence offers new opportunities to improve interview preparation through personalized questions, automated answer analysis, immediate feedback, and performance tracking. By combining Natural Language Processing (NLP) and Machine Learning (ML), the proposed system aims to provide candidates with an organized, interactive, and personalized environment for practising job interviews. The proposed system can help students, fresh graduates, and job seekers prepare for interviews according to their selected job roles. It can generate relevant interview questions, analyse candidate responses, identify areas requiring improvement, and provide feedback to support further practice. This approach may reduce the difficulties candidates face when preparing independently and help them develop a more structured interview preparation routine. Natural Language Processing plays an important role in analysing the relevance and meaning of answers. Techniques such as Cosine Similarity can be used to estimate the similarity between candidate responses and reference answers. Machine Learning can further support performance analysis by identifying patterns in practice results and highlighting areas where candidates may need additional preparation. These technologies can contribute to a more personalized learning experience by helping candidates focus on their individual weaknesses. The system may also provide features such as voice-based interview practice, speech-to-text conversion, coding questions, and performance reports. Voice-based practice can allow candidates to rehearse spoken responses, while coding questions can support preparation for technical positions. Performance reports can help users monitor their progress across multiple practice sessions and understand whether their answers are becoming more relevant, clear, and well-structured. Another important benefit of the proposed system is accessibility. Candidates can practise at their own pace, repeat questions, and review feedback without depending entirely on the availability of a human interviewer. This may be particularly useful for students and fresh graduates who have limited opportunities to participate in formal mock interviews. However, the actual benefits will depend on the system's design, usability, and the quality of its evaluation methods. Despite its potential advantages, AI-based interview evaluation has certain limitations. Automated feedback may be inaccurate when reference answers are incomplete or when candidates express correct information in different words. Machine Learning models may also produce biased or inconsistent results if the training data or evaluation criteria are unsuitable. In addition, collecting and storing personal information, interview responses, and voice recordings may create privacy and security concerns. Therefore, the proposed system should be developed with appropriate attention to transparency, fairness, reliability, and data protection. Candidates should be able to understand the basis of their evaluation, and their personal information should be handled securely. Human judgement should complement automated evaluation, particularly when assessing complex responses, practical knowledge, communication abilities, and other qualities that cannot be measured accurately through automated scores alone. Furthermore, the effectiveness of the proposed system should be examined through prototype testing and feedback from actual users. Future evaluation can consider answer relevance, consistency of scores, user satisfaction, ease of use, and improvements in interview readiness after repeated practice. Such testing would help identify the strengths and limitations of the system and provide opportunities for further development. In conclusion, an AI-based personalized interview preparation and candidate evaluation system can be a useful support tool for improving interview practice when designed responsibly and evaluated using actual user feedback. By combining AI technologies with structured practice and meaningful feedback, the system aims to help candidates strengthen their knowledge, improve their answer quality, and develop confidence. Its primary purpose should be to support continuous learning and interview readiness rather than replace human judgement or guarantee employment outcomes.

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