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Recruitment is a fundamental process in modern organizations, playing a crucial role in selecting qualified candidates efficiently. However, traditional hiring methods involve manual resume screening, written assessments, and interviews, which are time-consuming, labor-intensive, and prone to human bias. With the increasing number of job applications, organizations face significant challenges in processing candidate data and making accurate decisions.
This paper presents the design and implementation of an AI-based HR interview screening chatbot with an automated scoring system. The proposed system integrates multiple components, including resume parsing, Applicant Tracking System (ATS) scoring, online assessment evaluation, and AI-based chat screening, to streamline the recruitment process. Resume screening is performed using Natural Language Processing (NLP) techniques, where TF-IDF is used for feature extraction and machine learning models such as Support Vector Machine (SVM) are applied for classification.
The system also includes an online assessment module that evaluates candidates through multiple-choice questions, automatically calculating scores based on correct responses. Additionally, an AI-powered chatbot interacts with candidates to assess communication skills and generate intelligent screening scores. All candidate data and evaluation results are stored in a Snowflake database and visualized using Power BI dashboards for effective decision-making.
Experimental analysis shows that the proposed system significantly reduces manual effort, improves screening accuracy, and enhances the overall efficiency of the recruitment process.
"AI-BASED HR INTERVIEW SCREENING CHATBOT WITH AUTOMATED SCORING SYSTEM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b607-b611, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603170.pdf
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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