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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

TCR (Google Scholar): 4.11, TCR (Crossref): 13, g-index: 90, RJIF: 8.69

Peer Reviewed Journal

Vol. 11, Issue 10, Part C (2025)

Artificial intelligence in human resource management and employee well-being

Artificial intelligence in human resource management and employee well-being

Author(s)
Anjumanwarshaik, Sanober Shaikh, Preeti S Desai and Gireesh YM
Abstract
Artificial intelligence is just at the cusp of transforming Human Resource Management as we know it, with its direct implication now being on employee well-being. The paper undertakes a structured review of academic studies and industrial reports published between 2017 and 2025 to understand how AI is affecting HR functions and the psychosocial health of employees. The results suggest that AI helps in improving efficiency, agility, and personalized wellness intervention in HR; however, at the same time, it raises concerns about surveillance, job insecurity, and fairness. Drawing from socio-technical systems, social exchange, and conservation of resources theories, we propose a conceptual framework wherein the adoption of AI affects HR processes and employee well-being through mediators such as transparency, job security, and personalization, which, in turn, are moderated by the organizational context. The practical implications stress ethical AI adoption, transparent governance, and human oversight. The research gaps point at the need for longitudinal, cross-sector, and employee voice-centered studies. It comes to the conclusion that for AI to be an enhancement to rather than a threat to the human side of HR, it needs to strike a balance between efficiency and employee trust and well-being.
Pages: 219-224  |  1619 Views  1415 Downloads


International Journal of Applied Research
How to cite this article:
Anjumanwarshaik, Sanober Shaikh, Preeti S Desai, Gireesh YM. Artificial intelligence in human resource management and employee well-being. Int J Appl Res 2025;11(10):219-224. DOI: 10.22271/allresearch.2025.v11.i10c.12943
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