A Computational Approach to AI-Based Interview Simulations: Integrating Job Specific Question Generation, Speech Confidence Analysis, and Non-Verbal Cues

Traditional interview preparation techniques frequently fall short in giving individualized, real-time feedback due to the increased competitiveness in the job market. To improve candidates' readiness through real-time skill evaluation, this study offers an AI-powered mock interview platform that uses cutting-edge machine learning and natural language processing (NLP) techniques. The Meta Llama AI model, which is at the heart of the system, creates interview questions that are relevant to different professions and ensures that candidates are assessed on subjects that are pertinent to the sector. The platform uses speech recognition to examine hesitation patterns, tone, and voice modulation, providing information on a candidate's degree of confidence. To ensure that responses show clarity and depth of knowledge, NLP-based evaluation evaluates word fluency, sentence structure, and coherence. Additionally, facial recognition technology is used for real-time emotion analysis. This technology helps determine an interviewee's emotional stability and participation by detecting eye contact, facial expressions, and stress signs. The method uses several assessment characteristics, such as confidence level, response accuracy via keyword mapping, and overall involvement, to deliver a thorough performance review. An Employability Score, a measurable indicator that aids candidates in understanding their areas of strength and growth, is produced by combining these variables. This AI-driven method transforms interview preparation by providing structured feedback and tailored insights, making it more efficient, data-driven, and flexible to the changing demands of the labor market.