EXAMINING GENERATIVE AI USE IN HIGHER EDUCATION: LEARNING EFFICIENCY AND TECHNOLOGY DEPENDENCE
Maya Karmila

Universitas Terbuka

Abstract

The use of Generative Artificial Intelligence (GenAI) in student learning has increased significantly alongside the advancement of digital technologies in higher education. This study aims to analyze the role of GenAI in improving learning efficiency while examining the risk of technology dependence among students. A quantitative approach with a survey design was employed. Data were collected from 200 university students who actively use GenAI tools, particularly ChatGPT, for academic purposes through purposive sampling. A structured questionnaire was developed based on the Technology Acceptance Model (TAM), incorporating variables such as perceived usefulness, perceived ease of use, learning efficiency, and technology dependence. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among variables. The results indicate that perceived usefulness and perceived ease of use have a positive effect on GenAI usage, which enhances learning efficiency. However, increased usage also contributes to higher levels of technology dependence, potentially reducing cognitive engagement and independent learning. In conclusion, GenAI plays a dual role as both a facilitator of efficient learning and a source of potential dependency, requiring proper management, digital literacy, and ethical considerations in its implementation.

Keywords: generative ai- higher education- learning efficiency- pls-sem- technology dependence

Topic: AI for Learning