Learning Satisfaction as a Key Mechanism in Higher Vocational Students’ Continuance Intention to Use Generative AI for Computer Course Learning
Year: 2026
Type: book-chapter
Study design: Observational Study
Abstract: This study examines higher vocational students' continuance intention to use generative artificial intelligence (GAI) in computer course learning.Drawing on the Technology Acceptance Model and continuance-use research, it tests whether perceived usefulness, perceived ease of use, and learning engagement affect continuance intention through learning satisfaction.Data from 320 students in Heilongjiang Province, China, were analyzed using structural equation modeling and bootstrap mediation analysis.The results show that perceived usefulness, perceived ease of use, and learning engagement significantly enhance learning satisfaction, which has the strongest direct effect on continuance intention and mediates all three antecedent effects.Perceived usefulness does not directly affect continuance intention, whereas perceived ease of use retains a small but significant direct effect.The findings suggest that continued GAI use depends on whether students' perceptions and engagement are translated into satisfying, task-oriented learning experiences.
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7204993296