Assessing attitudes and impact of AI integration in higher education

Abstract

Purpose

This study aims to examine the integration of AI in student engagement and its benefits in the learning environment.

Design/methodology/approach

The study employed a quantitative research method, analyzing data from a sample of 720 students. The econometric data analysis used the structural equation modeling (SEM) technique.

Findings

The results show that facilitating conditions and performance expectations positively affect students’ attitudes toward AI, whereas the negative impact of perceived risk is statistically weak and only marginally significant at the 90% level. On the other hand, the main findings are that attitudes significantly influence the use of AI, which consequently increases students’ productivity, performance, and self-efficacy.

Research limitations/implications

Through the integration of new variables in the TAM and UTAUT models, steps are suggested for institutions to take to increase the acceptance and efficiency of AI.

Originality/value

This study introduces a novel approach to AI integration within higher education, presenting an innovative model that significantly enhances the discourse on AI’s tangible impacts on educational processes.

Citation

Baca, G. and Zhushi, G. (2024), "Assessing attitudes and impact of AI integration in higher education", Higher Education, Skills and Work-Based Learning, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/HESWBL-02-2024-0065