Mock AI Interviewer for Real-Time Feedback and Suggestion
The "Mock AI Interviewer for Real-Time Feedback and Suggestions" is an advanced AI-driven interview training system designed to transform the way candidates prepare for interviews by providing instant, data-driven feedback. Traditional interview preparation often lacks personalized insights and relies on subjective evaluation, leaving candidates uncertain about their strengths and areas for improvement. This system bridges that gap by analyzing both verbal and non-verbal cues— including speech patterns, grammatical accuracy, emotional expressions, and confidence levels—to generate a comprehensive, unbiased assessment of a candidate’s performance. Leveraging cutting-edge Natural Language Processing (NLP), machine learning models, and emotion detection algorithms, the system offers real-time evaluations, allowing users to refine their communication skills, body language, and overall presentation dynamically. Unlike conventional coaching methods that depend on human evaluators, this AI-powered solution ensures objective, scalable, and bias-free assessments, making professional interview training more accessible and efficient. A key feature of this system is its multimodal approach, integrating video, audio, and text- based analysis to deliver actionable feedback. Candidates receive instant suggestions on aspects like speech clarity, emotional stability, fluency, and posture, enabling them to make immediate adjustments and track their progress over multiple sessions. By providing adaptive, interactive, and intelligent coaching, this AI- powered mock interviewer not only enhances individual preparation but also represents a significant step forward in AI- driven hiring and career development. This research highlights the growing role of AI in revolutionizing interview training, equipping candidates with data-backed insights to excel in real- world interviews.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-05-15
- DOI
- https://doi.org/10.5281/zenodo.15421829
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 0.00