International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Driven Resume Parsing and Automated Interview Question Generation using NLP |
|---|---|
| ABSTRACT | The rapid growth of digital recruitment platforms has significantly increased the number of job applications, making manual resume screening time-consuming and inefficient. In addition, many candidates lack proper guidance for interview preparation. To address these challenges, this paper proposes an AI-Driven Resume Parsing and Automated Interview Question Generation System using Natural Language Processing (NLP). The system extracts key information such as skills, education, certifications, and experience from resumes and analyzes candidate profiles using machine learning and language models. Based on the extracted data, it generates personalized technical and HR interview questions to improve candidate readiness. The system is implemented using Python and deployed as a web-based application. Experimental results demonstrate that the system effectively parses resume data and generates relevant interview questions. The proposed approach enhances recruitment efficiency and supports candidates in interview preparation, highlighting the potential of AI in modern recruitment systems. |
| AUTHOR | A. HEMALATHA, K. TEJASWINI, B.PRAVEEN KUMAR, J.V.SANDEEP, M.ROSHINI, M.SAI KRISHNA Assistant Professor, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India Student, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403083 |
| pdf/83_AI-Driven Resume Parsing and Automated Interview Question Generation using NLP.pdf | |
| KEYWORDS | |
| References | [1] Y. LeCun et al., “Deep Learning,” Nature, 2015. [2] J. Devlin et al., “BERT: Language Understanding,” NAACL, 2019. [3] A. Vaswani et al., “Attention Is All You Need,” NIPS, 2017. [4] S. Bird et al., Natural Language Processing with Python, 2009. [5] D. Jurafsky & J. Martin, Speech and Language Processing, 2019. [6] T. Mikolov et al., “Word Representations,” ICLR, 2013. [7] J. Pennington et al., “GloVe,” EMNLP, 2014. [8] T. Brown et al., “Few-Shot Learners,” 2020. |
