The Effect of AI-Based Learning on University Students’ Learning Motivation

Abstract

Artificial intelligence has become a common learning aid in higher education, yet its contribution to motivation may coexist with risks to students’ cognitive autonomy. This study examined descriptive patterns in the influence of AI-based learning on the learning motivation of students enrolled in Computer Technology programs at universities in West Java, Indonesia. A descriptive quantitative design was applied to questionnaire responses from 30 students selected through simple random sampling as reported in the original study. The instrument contained six Likert-scale statements covering personalized learning, feedback speed, access to learning resources, critical thinking, independent creativity, and self-confidence. The analysis combined agree and strongly agree responses and converted them into percentages. The strongest positive response concerned easier access to and summarization of learning resources (90%), followed by rapid feedback (87%) and personalized understanding (83%). Moderate risk patterns also appeared, including reduced critical thinking (63%), lower confidence because of repeated AI checking (60%), and reduced independent creativity (57%). The findings indicated that AI-based learning supported motivation through efficiency, accessibility, and perceived competence, but unguided dependence could weaken independent reasoning. Lecturers should design process-oriented assessments, require verification of AI output, and maintain direct academic interaction. Because the study used a small sample and descriptive analysis, the results should be treated as preliminary rather than causal.