The Impact Of Trust In AI On Inspiration And Intention To Use AI In Teaching Among University Lecturers

Abstract

This study investigates the relationship between trust in AI, inspiration, and faculty intention to use AI in teaching, integrating the technology acceptance model (TAM) and cognitive emotion theory (CET). Using partial least squares structural equation modeling (PLS-SEM), data from 332 university lecturers in Ho Chi Minh City were analyzed. The results reveal that trust in AI outcomes (β = 0.441) and inspiration (β = 0.412) significantly and positively influence intention to use AI in teaching. Notably, inspiration mediates the relationship between trust and intention (indirect effect β = 0.213), highlighting its critical role in translating trust into actionable intentions. The proposed model explains 59.5% of the variance in AI usage intention, confirming its robustness. Additionally, trust in AI outcomes strongly predicts inspiration (β = 0.517), emphasizing the importance of fostering both trust and emotional engagement to enhance AI adoption in higher education. Based on these findings, the study suggests practical strategies, including developing an “AI Trust Ecosystem,” launching an “AI Inspiration Movement,” and implementing an “AI-Integration Roadmap”. These strategies aim to build trust, inspire faculty, and ensure effective AI integration in teaching practices. The study contributes to expanding TAM by incorporating emotional factors from CET, offering a novel perspective on technology acceptance in education.