The study explores the integration of intelligent web scraping techniques to enhance the internship matching process within internship management systems. The increasing demand for internships necessitates timely and efficient intern matching, a task that conventional manual techniques need help with due to its complexity and time-consuming nature. Intelligent web scraping algorithms and machine learning techniques analyze extensive datasets to match interns with businesses based on competencies, interests, and professional objectives. The integration leverages natural language processing to extract relevant information from internship listings and candidate profiles, enhancing the precision and effectiveness of the matching process. Additionally, clustering and matching algorithms refine recommendations, pairing students with opportunities that fit their competencies and career objectives. However, implementing intelligent web scraping raises ethical concerns, particularly regarding data privacy and algorithmic bias. Ensuring the ethical utilization of these techniques is critical for fair and unbiased internship matching. The research addresses these ethical considerations while proposing a framework for integrating intelligent web scraping into existing systems. The study reviews the literature on web scraping and machine learning in internship management, critically analyzing and synthesizing past research findings to demonstrate the efficacy of these techniques over conventional methods. The study also introduces a theoretical model for effective internship matching, investigating intelligent web scraping and machine learning techniques to optimize the process. Additionally, it examines the benefits, challenges, and limitations of integrating these techniques. The proposed intelligent web scraping approach simplifies internship matching, aligns student strengths with opportunities, enhances onboarding efficiency, and bridges academic learning with practical application. © 2025 by the author(s).
Author Keywords
Index Keywords
