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The rapid growth of job seekers and competitive hiring processes has created a need for intelligent systems that can effectively prepare candidates and guide them toward suitable career opportunities. This paper proposes **Smart Mock AI**, an integrated interview engine and job recommendation simulator designed to enhance candidate readiness and optimize job matching.
The system leverages artificial intelligence to simulate real-time interview scenarios, including technical, behavioral, and aptitude-based questions, providing dynamic feedback and performance analysis. It incorporates natural language processing (NLP) techniques to evaluate candidate responses and identify strengths and weaknesses. In addition, a recommendation engine is integrated to suggest relevant job roles based on user skills, performance metrics, and preferences.
The job recommendation module utilizes machine learning algorithms to analyze user profiles and match them with appropriate job opportunities, improving decision-making efficiency. Recommender systems are widely used to filter and personalize choices based on user behavior and preferences ([Wikipedia][1]). The proposed system combines interview simulation with recommendation capabilities, offering a unified platform for career preparation.
Experimental results demonstrate that Smart Mock AI improves interview performance, increases confidence among candidates, and provides accurate job recommendations. This system has potential applications in placement preparation, career counseling, and recruitment platforms, contributing to smarter and more efficient hiring ecosystems.
Keywords:
Artificial Intelligence, Machine Learning, Natural Language Processing, Mock Interview System, Job Recommendation System, Interview Simulation, Skill Assessment, Recommender Systems
Cite Article:
"Smart Mock AI: Interview engine and Job Recommendation Simulator", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a399-a404, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605045.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator