Exploring the AI-powered Adoption in Higher Education: A Comprehensive Study Using UTAUT4 Model to Understand User Acceptance and Usage

  • Sajeela Ashfaque Tago Institute of Mathematics & Computer Science, University of Sindh, Jamshoro, Pakistan
  • Ayaz Keerio Institute of Mathematics & Computer Science, University of Sindh, Jamshoro, Pakistan
  • Shahmurad Chandio Institute of Mathematics & Computer Science, University of Sindh, Jamshoro, Pakistan
  • Altaf Hussain Abro Institute of Mathematics & Computer Science, University of Sindh, Jamshoro, Pakistan
Keywords: AI-powered learning, UTAUT4, Contextual awareness, Personal innovativeness, Self-direct learning

Abstract

AI-powered learning is an innovative, student-centered educational paradigm integrating formal, informal, and social learning modalities. This study examines the acceptance of AI-powered learning in higher education institutions in Pakistan, concentrating on student acceptance and usage. Contextual awareness, self-directed learning, Personal innovativeness, and performance expectancy Factors were examined using the Smart-PLS approach to evaluate structural relationships and test hypotheses based on the expanded Unified Theory of Acceptance and Use of Technology (UTAUT4). The Results indicate substantial positive correlations between the proposed variables and students' acceptance of AI-powered learning methods. The findings offer significant insights into the structures that may influence the utilization and subsequent outcomes of AI-powered learning acceptance & usage in HEI, including Pakistan, and the UTAUT4 model offers a useful guide for decision-makers and educational institutions working on m-learning adoption at universities.

Published
2024-12-26
How to Cite
[1]
Sajeela Ashfaque Tago, Ayaz Keerio, Shahmurad Chandio, and Altaf Hussain Abro, “Exploring the AI-powered Adoption in Higher Education: A Comprehensive Study Using UTAUT4 Model to Understand User Acceptance and Usage”, J. ICT des. eng. technol. sci., vol. 8, no. 2, pp. 6-10, Dec. 2024.
Section
Articles