Technology Acceptance and Faculty Readiness for AI-Integrated Teaching in Higher Education

Abstract

The recent integration of artificial intelligence (AI) into higher education has altered educational processes, but faculty preparedness to teach with AI is uneven, especially in developing countries. Though previous studies have comprehensively examined students' technology acceptance, scant empirical research has examined faculty members' readiness to embrace AI tools and integrate the technology into their pedagogy. Based on the Technology Acceptance Model, this paper examines the association between technology acceptance variables and the faculty preparedness to AI-based embedded learning in Indian institutions of higher education in Punjab, Pakistan. The research used a quantitative correlational design. The data were collected from 350 faculty members via stratified random sampling across public and private universities. Perceived usefulness, perceived ease of use, attitude towards AI, AI self-efficacy, and faculty preparation to teach using AI were measured using a structured questionnaire. The proposed hypotheses were tested using descriptive statistics, Pearson correlation analysis, and multiple regression. The results show that perceived usefulness, attitude to AI, and AI self-efficacy have a strong positive correlation, which predicts faculty readiness to use AI-integrated teaching, whilst perceived ease of use has a relatively weaker but positive relationship. The model shows a high proportion of variance in faculty readiness, with both psychological and cognitive variables playing crucial roles in AI adoption. The research has a role to play in generalizing technology acceptance theory to the realm of AI-based pedagogy, as well as in effectively implementing institutional policy, faculty development curricula, and strategic digital transformation programs in higher education.