Scholarlink

<p>The present research integrates the Theory of Planned Behavior (TPB) with the ARCS Motivation Model to describe the elements influencing students’ intentions to use AI-based Personalized Learning Environments (PLE) in higher education. The study intends to offer insights into how behavioral intentions (Attitudes, Subjective Norms, and Perceived Behavioral Control) and motivation variables (Attention, Relevance, Confidence, and Satisfaction) together predict the use of AI-based PLE. Using structural equation modeling (SEM) and a correlational research approach, the study examines data from 725 students in a Turkish state university. Results show that whilst confidence and satisfaction have both direct and indirect effects on perceived behavioral control and subjective norms, attention and relevance indirectly influence behavioral intentions through attitudes. The results highlight the need of creating interesting, relevant, and confidence-boosting AI-based PLE as well as of a favorable social context to improve technology use. Practical insights provided by this multidisciplinary approach help instructors and developers design more successful and interesting AI-based learning systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Understanding students’ intentions to use of AI-Based personalized learning environments in higher education: A TPB and ARCS model approach

  • 期刊论文

  • 发表日期：

    2025年12月23日

查看全文
  • Bilge Aslan Altan,
  • Güler Göçen Kabaran

摘要

The present research integrates the Theory of Planned Behavior (TPB) with the ARCS Motivation Model to describe the elements influencing students’ intentions to use AI-based Personalized Learning Environments (PLE) in higher education. The study intends to offer insights into how behavioral intentions (Attitudes, Subjective Norms, and Perceived Behavioral Control) and motivation variables (Attention, Relevance, Confidence, and Satisfaction) together predict the use of AI-based PLE. Using structural equation modeling (SEM) and a correlational research approach, the study examines data from 725 students in a Turkish state university. Results show that whilst confidence and satisfaction have both direct and indirect effects on perceived behavioral control and subjective norms, attention and relevance indirectly influence behavioral intentions through attitudes. The results highlight the need of creating interesting, relevant, and confidence-boosting AI-based PLE as well as of a favorable social context to improve technology use. Practical insights provided by this multidisciplinary approach help instructors and developers design more successful and interesting AI-based learning systems.