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FACTORS INFLUENCING UNIVERSITY STUDENTS’ INTENTION TO USE
ARTIFICIAL INTELLIGENCE TOOLS FOR LEARNING:
QUANTITATIVE EVIDENCE FROM VIETNAM

Vu Thi Mai Duyen* 1

1 Thanh Dong University, Hai Phong, Vietnam.
(*E-mail: duyenvtm@thanhdong.edu.vn)

ABSTRACT
This study examines the factors influencing university students’ intention to use
artificial intelligence (AI) tools for learning in Vietnam. The study is timely because the
rapid diffusion of generative AI is reshaping higher education at the same time that
Vietnam is accelerating national and sectoral digital transformation. Drawing on the
Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of
Technology (UTAUT), and the Theory of Planned Behavior (TPB), the study tests five
explanatory variables: perceived ease of use, perceived usefulness, social influence, AI
self-efficacy, and trust in AI. The dataset comprises 210 valid responses and is analyzed in
SPSS using Cronbach’s Alpha, exploratory factor analysis, Pearson correlation, and
multiple regression. After scale refinement, SI3, AISE4, AISE5, TR3, and TR4 were removed.
The final model retained 25 observed variables, achieved KMO = 0.805, Bartlett’s Test Sig.
= 0.000, cumulative explained variance of 62.963%, and adjusted R² = 0.805. All five
predictors positively and significantly influenced students’ intention to use AI tools for
learning, with perceived usefulness exerting the strongest effect. The findings provide
empirical evidence for universities seeking to design policies, guidance, and pedagogical
support for the responsible and effective use of AI in learning.
Keywords: Artificial intelligence tools; perceived usefulness; self-efficacy; university
students; use intention.

1. Introduction
Artificial intelligence, especially generative AI, is rapidly changing how university
students search for information, summarize materials, generate ideas, solve problems,
and complete academic tasks. Compared with earlier digital technologies that mainly
supported storage, communication, or presentation, current AI tools intervene more
directly in learning processes by offering explanations, suggestions, drafting support, and
adaptive assistance. This shift has created major opportunities for personalized learning,
time efficiency, and broader access to academic support. At the same time, it has raised
concerns about output accuracy, trustworthiness, academic integrity, overreliance, and
the quality of independent thinking.
The urgency of this issue is especially evident in Vietnam. The National Digital
Transformation Program to 2025, with orientation to 2030, identifies education as a
priority domain of digital transformation [1]. More specifically, Decision No. 131/QD-TTg
on strengthening information technology application and digital transformation in
education and training emphasizes digital infrastructure, digital resources, and digital
capacity development across the sector, including higher education [2]. These policy
directions mean that universities are no longer only experimenting with digital tools; they
are expected to build structured digital learning ecosystems and guide students in the

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