Abstract
This study examines how perceived characteristics of AI chatbots affect the self-learning ability of students at Ho Chi Minh City University of Technology and Engineering and clarifies the mediating roles of learning self-efficacy and learning motivation. Survey data from 356 students were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. The results support most of the proposed hypotheses, and the model explains a substantial proportion of the variance in self-learning ability. Learning self-efficacy has the largest total effect, followed by learning motivation. Notably, most chatbot characteristics influence self-learning mainly through learners’ internal psychological mechanisms rather than through direct effects. The study recommends strengthening self-efficacy, sustaining motivation, and creating guided, critical, and responsible conditions for chatbot-supported learning.
AI chatbot
self-learning ability
learning self-efficacy
learning motivation
PLS-SEM