Feby Anggraini, Sri Meutia, Defi Irwansyah

Analysis of The Influence of Student Decisions in Using Artificial Intelligence (AI) As a Learning Reference

Introduction

Analysis of the influence of student decisions in using artificial intelligence (ai) as a learning reference. Explore how Industrial Engineering students' decisions to use AI as a learning reference are shaped by perceived benefits, ease of use, and actual usage. Insights for AI integration in higher education.

Abstract

Education is one of the many facets of human existence that have changed as a result of the advancement of artificial intelligence (AI). The goal of artificial intelligence (AI), a subfield of computer science, is to create computers and systems that can carry out operations that normally call for human intellect. According to data, the number of people using AI is expected to reach 3.33 million annually by 2030. Out of the 100 respondents who received the surveys, 100 said they utilize AI apps. Students in semesters two (19%), four (36%), six (17%), and eight (28%), respectively, make up the distribution of use. 90% of AI is used as a learning reference, compared to 10% for thesis. This study uses the Technology Acceptance Model (TAM) technique as a theoretical framework to ascertain the perceptions that help Industrial Engineering students decide whether to use AI as a learning reference. The analysis's findings indicate that perceived advantages (38.64%), perceived ease of use (40.73%), and actual usage (16.81%) are the perceptions that affect students' decisions in Malikussaleh University's Industrial Engineering department. The development of AI integration tactics in higher education can benefit greatly from these discoveries.

Review

The paper titled "Analysis of The Influence of Student Decisions in Using Artificial Intelligence (AI) As a Learning Reference" addresses a highly relevant and timely topic in contemporary education: the adoption of AI tools by students. Given the rapid advancements and increasing integration of AI across various sectors, understanding student perceptions and decision-making processes regarding AI as a learning reference is crucial for educators and policymakers. The study aims to identify the factors influencing Industrial Engineering students' decisions to utilize AI, offering insights into a dynamic area of technological adoption within academic settings. This contributes to the growing body of literature on educational technology and user acceptance. Employing the Technology Acceptance Model (TAM) as its theoretical framework, the research surveyed 100 Industrial Engineering students from Malikussaleh University. It's noted that all 100 respondents confirmed their use of AI applications, with a significant majority (90%) utilizing AI as a learning reference, and the remainder for thesis work. The distribution of use across different academic semesters (2, 4, 6, 8) is also provided, indicating broad engagement among students at various stages of their studies. The core findings highlight that perceived ease of use (40.73%), perceived advantages (38.64%), and actual usage (16.81%) are the primary perceptions influencing students' decisions to adopt AI for learning. These quantitative insights are valuable in pinpointing specific drivers of AI adoption within this student demographic. A clear strength of this study lies in its direct application of a well-established model (TAM) to a novel and important context, providing specific percentages for the influence of key factors. The explicit focus on Industrial Engineering students at a particular university, while providing specific data, also suggests a potential limitation in generalizability beyond this specific context and discipline. Nevertheless, the findings offer practical implications for higher education institutions, particularly for developing targeted strategies to integrate AI effectively. Understanding that perceived ease of use and perceived advantages are dominant factors can guide the design of AI tools and training programs to enhance their adoption and maximize their benefits as learning references. Future research could explore these factors in diverse student populations and academic disciplines to build upon these initial findings.

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