An Intelligent Decision-Support System for Academic Admissions Using NLP and Machine Learning

Fatima Abid; Kashmala Mujahid; Muhammad Naeem; Muhammad Salman Ahmad; Hamid Ghous; Mubasher Hussain Malik; Muhammad Allah Razi; Ghulam Muhy Ud Deen Raee

doi:10.5281/zenodo.20433883

المؤلفون

Fatima Abid Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan المؤلف
Kashmala Mujahid Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan المؤلف
Muhammad Naeem Department of Computer Science, The Islamia University of Bahawalpur (IUB), Bahawalpur, Punjab, Pakistan المؤلف
Muhammad Salman Ahmad Department of Computer Science, The Islamia University of Bahawalpur (IUB), Bahawalpur, Punjab, Pakistan المؤلف
Hamid Ghous Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan المؤلف
Mubasher Hussain Malik Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan المؤلف
Muhammad Allah Razi Department of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology (KFUEIT), Rahim Yar Khan, Punjab, Pakistan المؤلف
Ghulam Muhy Ud Deen Raee Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan المؤلف

DOI:

https://doi.org/10.5281/zenodo.20433883

الكلمات المفتاحية:

CV Summarization; Natural Language Processing (NLP); Named Entity Recognition (NER); Extractive Summarization; Abstractive Summarization; Candidate Ranking; Conversational AI; Higher Education Admissions

الملخص

The manual screening of thousands of applications for Master’s degree programs at Hassan II University of Casablanca represents a highly time-consuming and labor-intensive process. To address this challenge, this study proposes an intelligent automated framework based on Natural Language Processing (NLP) for large-scale CV summarization and candidate ranking. The proposed system integrates pre-trained spaCy and Hugging Face Transformer-based Named Entity Recognition (NER) models to extract critical candidate information, including educational background, professional experience, and technical skills. The framework combines both extractive and abstractive summarization techniques to generate concise and meaningful candidate profiles. Extractive summarization is performed using BERT-based models to identify the most informative sentences within each CV, while abstractive summarization employs advanced Large Language Models (LLMs), particularly LLaMA, to generate coherent and contextually refined summaries. In addition, the system incorporates a semantic candidate-ranking module designed to evaluate applicant suitability according to program-specific requirements. To validate the effectiveness of the proposed framework, a case study was conducted using 2,325 CVs submitted to the Master’s program in Big Data and Data Science. Experimental evaluation demonstrated strong performance, achieving ROUGE-1 Recall of 72.67%, ROUGE-2 Recall of 74.32%, ROUGE-1 Precision of 73.15%, ROUGE-2 Precision of 57.28%, and Named Entity Recognition (NER) Precision of 82%. The system processed each CV in an average time of 3.84 seconds, demonstrating its suitability for large-scale admissions environments. Furthermore, a conversational AI assistant (chatbot) was integrated into the framework, enabling admissions evaluators to interactively query uploaded CVs in real time. This functionality significantly improves decision-making efficiency, enhances candidate exploration, and reduces administrative workload. Overall, the findings demonstrate that NLP-driven automation provides a scalable, efficient, and intelligent solution for modern academic admissions and candidate screening systems.

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