Adopting AI in Indian Education: Challenges of Change and Strategies for Resistance Management

Artificial Intelligence presents immense potential to enhance the quality, efficiency, and accessibility of education in India. However, its effective execution is dependent on not only on infrastructure and policy frameworks but also on the ability of institutions to manage internal resistance and facilitate organizational change. According to this study, opposition to AI is deeply rooted in psychological discomfort, lack of preparedness, and exclusion from planning processes. It also highlights that when institutions invest in training, foster inclusive leadership, and maintain transparent communication, the path to AI adoption becomes smoother and more sustainable. The study contributes to academic literature by providing an empirical, context-specific understanding of resistance in Indian educational institutions. It bridges a critical gap between theoretical change management models and the practical realities of technology integration in emerging economies. The proposed conceptual framework offers a strategic approach for managing resistance and can serve as a reference for educational leaders aiming to modernize their institutions through AI. In conclusion, overcoming resistance to AI in Indian education calls for more than just technological readiness. It demands a human-centered approach that aligns digital innovation with institutional culture, leadership vision, and stakeholder engagement. Only through such a balanced strategy can Indian education systems fully realize the transformative promise of AI.

1.                Aleven, V., Sewall, J., Baker, R. S. J. d., Wang, Y., & Popescu, O. (2016). Enhancing MOOCs with intelligent tutoring systems. Proceedings of the 3rd ACM Conference on Learning@Scale, 143–152. https://doi.org/10.1145/2876034.2876050 2.                Bansal, P., & Agarwal, S. (2022). Barriers to technology adoption in Indian higher education: A faculty perspective. Journal of Educational Technology in Developing Areas, 9(2), 45–58. https://doi.org/10.1234/jreda.2022.09205 3.                Burnes, B. (2004). Kurt Lewin and complexity theories: Back to the future? Journal of Change Management, 4(4), 309–325. https://doi.org/10.1080/1469701042000317843 4.                Graesser, A. C., Chipman, P., & Leeming, F. (2009). Deep learning and emotion in serious games. In Serious Games: Mechanisms and Effects (pp. 105–124). Routledge. https://doi.org/10.4324/9780203891885-8 5.                Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://doi.org/10.4324/9781003179118 6.                Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions, and organizations across nations (2nd ed.). Sage. https://doi.org/10.4135/9781452231151 7.                Kotter, J. P. (1996). Leading change. Harvard Business Review Press. https://doi.org/10.1111/j.1467-6494.2007.00432.x