Generative AI and the Transformation of Academic Work: A Conceptual Framework for Faculty Adaptation, Professional Identity Reconstruction, and Organizational Change in Indian Higher Education

Abstract

Generative AI (GenAI) is rapidly reshaping higher education by altering teaching, assessment, research, and service roles. This paper develops a theory-driven conceptual framework for understanding how Indian university faculty adapt to GenAI, emphasizing that adaptation entails far more than mere tool adoption. Synthesizing literature on technology adoption (TAM), institutional theory, dynamic capabilities, professional identity, and sociological analyses of work, we argue that faculty adaptation involves complex processes of identity reconstruction, digital skill development, and organizational change. GenAI-driven academic work transformation (e.g. AI-assisted teaching, automated assessment, content generation) offers opportunities for personalization and productivity, but also raises ethical, privacy, and labor concerns. Faculty responses reflect ambivalence: they acknowledge AI’s usefulness yet fear deskilling and displacement. Adaptation thus requires not only perceiving AI tools as useful and easy-to-use (per TAM) but also developing AI literacy and competency, overcoming resistance, and re-envisioning one’s professional role. We integrate Dynamic Capability Theory (sensing, seizing, reconfiguring) to show how universities sense AI trends, seize resources (training, infrastructure), and reconfigure teaching/research processes. Professional Identity Theory explains how faculty must reconstruct identities (e.g. expert → facilitator/designer) under shifting professional logics. Institutional theory highlights external pressures (policy, accreditation, peer norms) that drive organizational adaptation and legitimize new practices. The resulting framework links individual factors (AI literacy, digital competency) and organizational factors (support, readiness) to faculty adaptation, identity reconstruction, and organizational change, with dynamic capabilities enabling the process (Figure 1). From these insights we propose 12 conceptual propositions. Contributions include extending technology adoption and organizational change theories into the domain of higher education, illuminating the identity work entailed by AI, and guiding university leaders on enabling resilient academic innovation. Practical implications emphasize faculty development in AI literacy, ethical policies, and leadership support. We conclude with a research agenda calling for longitudinal and cross-cultural studies to test and refine the framework.

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