The Impact Of Artificial Intelligence Usage On Learning Effectiveness and Academic Motivation Among University Students - Ariel Febrian; Zidni Ma'ruf; Keyza Ariq Ronaldi; Royan Hidayah (Jurnal Pendidikan Teknologi Informasi dan Vokasional, 2026)
The rapid development of artificial intelligence (AI) has increasingly influenced higher education, including the emergence of AI-based learning applications such as ChatGPT, DeepSeek, and Perplexity. While prior studies have examined the role of AI in general academic settings, limited attention has been given to its use within vocational, practice-based education, where learning outcomes are closely tied to technical skill development. This study aims to describe vocational students' perceptions of AI use in learning, particularly in relation to learning effectiveness and academic motivation, among students at a vocational institution in Palembang. A quantitative descriptive design using the survey method was employed, involving 50 respondents selected through purposive sampling based on their active use of AI-based learning applications. Data were collected through a 15-item Likert-scale questionnaire distributed online and analyzed descriptively using frequency and percentage distributions. The findings indicate that most respondents held positive perceptions of AI, particularly regarding its role in helping them understand lecture material, complete assignments more efficiently, and study independently. However, perceptions were more divided regarding the accuracy of AI-generated information and its contribution to time management, suggesting that the perceived benefits of AI are not uniform across all aspects of learning. Respondents also emphasized that AI cannot replace the role of lecturers. These findings suggest that, when used appropriately alongside critical thinking and lecturer guidance, AI is perceived by vocational students as a supportive tool for practice-based learning. This study is limited by its small sample size, single-institution scope, and reliance on self-reported data without inferential analysis, and future research employing larger samples, validated instruments, and objective learning measures is recommended.Keywords: Artificial Intelligence; Learning Effectiveness; Academic motivation; Vocational Education; Vocational Student