A Study of Enhancing Training Effectiveness ‎PersonalizedEmployee-Development through ‎Generative AI (West Region Mid-Management IT ‎Employees-India)‎

  • Authors

    https://doi.org/10.14419/mng2fd65

    Received date: June 20, 2025

    Accepted date: September 2, 2025

    Published date: September 17, 2025

  • Employee Training; Effectiveness; Digitalization; Artificial ‎Intelligence

  • Abstract

    This paper develops a theoretical framework for understanding ‎the economic implications of AI-driven personalized training ‎systems in India’s information technology sector. Drawing ‎from human capital theory, technological research, and ‎organizational economics, we propose that AI-enhanced training ‎can address the fundamental challenges ‎facing IT workforce development. The framework synthesizes ‎existing economic theories with emerging research on AI-human ‎collaboration to identify key variables, propose testable ‎hypotheses, and outline implementation considerations for ‎Indian IT organizations. While empirical validation remains ‎necessary, this theoretical synthesis provides a foundation for ‎understanding how AI training systems might transform ‎workforce development economics in developing economic ‎contexts‎.

  • References

    1. A. Agrawal, J. Gans, and A. Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence. Cambridge, MA, USA: ‎Harvard Business Press, 2018.‎
    2. J. Bessen, “AI and jobs: The role of demand,” ‎Nat. Bur. Econ. Res., Working Paper, 2019.‎ https://doi.org/10.3386/w24235.
    3. ‎Deloitte Insights, “AI in the workplace: ‎Trends and impacts,” 2023. [Online]. Available: https://www2.deloitte.com.‎
    4. ‎McKinsey & Company, “The future of work ‎in India: AI and digital transformation,” ‎‎2022. [Online]. Available: ‎https://www.mckinsey.com.‎
    5. ‎A. Agrawal, J. Gans, and A. Goldfarb, “Economic policy for artificial intelligence,” Econ. ‎Policy, vol. 38, no. 116, pp. 485–528, Apr. ‎‎2023.‎
    6. D. Acemoglu and P. Restrepo, “Artificial in-‎intelligence, automation, and work,” Ind. Labor ‎Relation Rev., vol. 77, no. 2, pp. 245–282, Feb. ‎‎2024.‎
    7. ‎L. F. Katz and A. B. Krueger, “The rise of al-‎alternative work arrangements and digital platforms,” J. Hum. Resour., vol. 58, no. 4, pp. ‎‎1123–1156, Oct. 2023.‎
    8. ‎A. Banerjee and E. Duflo, “Technology adoption and human capital formation in developing economies,” J. Dev. Econ., vol. 167, pp. ‎‎103– 125, Jan. 2024.‎
    9. ‎M. Dell, B. F. Jones, and B. A. Olken, “Technology and human capital: Evidence from ‎the information age,” Rev. Econ. Stat., vol. ‎‎105, no. 4, pp. 892–908, Nov. 2023.‎
    10. ‎D. Card, J. Kluve, and A. Weber, “Active labor market policy evaluations: A meta-analysis of digital training programs,” Labor ‎Econ., vol. 78, pp. 101–118, Mar. 2024.‎
    11. ‎C. Goldin and J. Mitchell, “The race between ‎education and technology in the digital age,” ‎J. Econ. Perspecta., vol. 37, no. 3, pp. 45–68, ‎Summer 2023.‎

  • Downloads

  • How to Cite

    Khot, A. (2025). A Study of Enhancing Training Effectiveness ‎PersonalizedEmployee-Development through ‎Generative AI (West Region Mid-Management IT ‎Employees-India)‎ (D. R. . Goyal , Trans.). International Journal of Accounting and Economics Studies, 12(5), 710-718. https://doi.org/10.14419/mng2fd65