Exploring the Adoption of AI-driven Pedagogical Tools by Teachers- Trainers in India's Higher Education Sector

Objective:The use of AI tools is becoming integrated into teaching, training and administrative operations in Indian higher education. This research addresses factors that influence behavioral intention to adopt AI-enabled tools for teaching and training for teachers and organizational stakeholders in Indian higher education. Design/methodology/approach: Guided by the Technology Organization Environment (TOE) framework, a comprehensive adoption model was proposed and tested by integrating the technological, organizational and environmental determinants. Survey data were collected from 680 teachers and academic decision makers across Indian universities and colleges, which were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings: The findings suggest that the intention to adopt AI are primarily driven by technological capability factors, specifically perceived intelligence and perceived interactivity. Organisational readiness and top management support also significantly influence adoption intention. From the environmental perspective, vendor support and competitive pressure showed a significant positive influence while perceived benefit and perceived risk were not found to influence adoption intention significantly, signaling a shift from outcome based appraisal towards the implementation issues in the decision of AI adoption. Originslity/Value: Three original contributions are identified in this research: First, it expands the scope of inquiry in the area of AI adoption in higher education from students and broad faculty members to teacher-trainers which is one of the critical yet under-researched stakeholders who ultimately determine the future pedagogy; Second, it advances the existing T-O-E framework by adding specific technological characteristics associated with AI in explaining smart system adoption; Finally, it provides a unique empirical data set from a developing country context thus contributing to the external validity of AI adoption theories