Optimizing Human Resource Management in Education through Artificial Intelligence: A Quantitative Study

Authors

  • Yasmin Mirzani Department of Faculty of Management Studies, Banasthali Vidyapith, Jaipur, India
  • Anshuman Shastri Centre of Artificial Intelligence, Banasthali Vidyapith, Jaipur, India,

DOI:

https://doi.org/10.70917/ijcisim-2026-4674

Keywords:

Artificial Intelligence, Human Resource Management, Machine learning algorithm, Education industry, Employee development, data privacy

Abstract

This article examines the role of artificial intelligence (AI) in enhancing human resource management (HRM) within the education industry. Building upon recent growth, it examines how AI-enabled tools automate repetitive HR tasks, support data-driven decision-making, and augment strategic functions such as recruitment, workforce planning, and staff development. A quantitative research design was adopted, with a purposive sample of 50 HR professionals from educational institutions. Data was collected through a structured questionnaire using a five-point Likert scale and analyzed with descriptive statistics. Findings indicate high levels of awareness of AI among HR professionals and strong agreement that AI improves recruitment accuracy, workforce planning and personalized training. Respondents also acknowledged AI’s value in enhancing employee engagement and supporting data-driven HR decision-making, although concerns about data privacy and reduced human interaction remain. The study contributes to ongoing debates on digital transformation in HRM by providing empirical evidence from the education sector and suggests directions for both practice and future research.‬ 

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Published

2026-08-12

How to Cite

Yasmin Mirzani, & Anshuman Shastri. (2026). Optimizing Human Resource Management in Education through Artificial Intelligence: A Quantitative Study. International Journal of Computer Information Systems and Industrial Management Applications, 18(16s), 1388–1412. https://doi.org/10.70917/ijcisim-2026-4674

Issue

Section

Original Articles