Higher Education Students' Attitudes Towards Artificial Intelligence - A Comparison of SPSS And Machine Learning

Aug 3, 2026

The study examines higher education students’ attitudes toward artificial intelligence and analyzes how knowledge, confidence, and usage relate to each other. Data come from an online questionnaire targeting diverse student demographics. Beyond traditional statistical methods, machine learning algorithms are applied to explore quantitative relationships and to evaluate machine learning’s potential for interpreting findings about AI awareness and confidence. The work also highlights opportunities and challenges.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Literature Review

Methodology

Questionnaire and Data Collection

SPSS and Machine Learning Analysis

Results and Discussion

Conclusion

Higher Education Students' Attitudes Towards Artificial Intelligence: A Comparison of SPSS And Machine Learning František Ribní1,2 1Institute of Education, ELTE Eötvös Lóránd University, Budapest, Hungary 2Doctoral School of Education, ElTE Eötvös Lóránd University, Budapest, Hungary ABSTRACT The rise of AI (Artificial Intelligence) in education is attracting increasing attention. The integration of AI-based tools and methods in higher education can not only improve the quality of education, but also open new perspectives for students. In addition to the growing interest in AI, an important question is the level of awareness and confidence of students in this technology. While there are many studies on the role ofAI in education (Luckin et al., 2016; Holmes et al., 2019), few studies have examined students' knowledge and confidence in AI and the frequency of its use. A questionnaire survey is used to collect the data. The questionnaire survey is conducted through an online platform, targeting different demographic groups in higher education. In addition to traditional statistical methods, AI-based machine learning algorithms are used to analyze the data. Using statistical methods, we explore quantitative relationships between students' AI knowledge and confidence. At the same time, we explore the potential of machine learning in data analysis, because while we initially analyzed their attitudes towards AI, we now explore the opportunities and challenges that machine learning offers for interpreting research findings. KEYWORDS artificial intelligence, higher education students, machine learning, ChatGPT, SPSS, ChatGPT Data Analyst INTRODUCTION Nowadays, AI (Artificial Intelligence, also known as Machine Learning) and its scope are at the center of discourse, across a very broad spectrum, from its present application to its future usability. Consequently, many disciplines are concerned with their applicability in real life. Whether it is to facilitate human work (as a virtual assistant (Cheng et al., 2023; Pandey et al., 2023) or even as an aid in education (González et al., 2022) . The interest just discussed is not surprising, as it is a relatively new technology, although, if we look deeper, the foundations of machine learning go back further. The first system capable of learning was the McCulloch-Pitts, also known as the Perceptron model, pioneered by Warren McCulloch and Walter Pitts, and the model is credited to them. The model, or idea, was published in 1943 and effectively laid the foundations for the neural networks on which modern machine learning and AI are based (Pickover, 2024; Macukow, 2016; McCulloch, 1943). This recognition reflects the fact that, although the current technological developments are new, the underlying ideas are the result of decades of development. It is an understanding of this long evolutionary process that highlights that the rise ofAI brings not only benefits and opportunities, but also complex challenges that can only be successfully addressed if the application of technology is accompanied by a deep and comprehensive understanding. The aim of this study is to explore the knowledge, use and confidence of higher education students in using AI. Second, we explore the potential of machine learning in data analysis, given that while we initially analyzed students' attitudes towards AI, now we explore the opportunities and challenges that machine learning offers for interpreting research findings alongside traditional statistical methods. As the above-mentioned developments show, we also need to make progress in the application and use of AI. A thorough understanding of the technology is essential to exploit its potential. LITERATURE REVIEW AI and its subject matter are one of the hottest topics of our time. The Web of Science database has published more than 400 articles over the last few years that explore the intersection of keywords between students in higher edu

FAQs

What does the study investigate about higher education students and AI?

It explores students’ knowledge, use, and confidence regarding artificial intelligence, and examines how these factors relate to their attitudes toward AI.

How is the data collected in this research?

The research uses an online questionnaire survey targeting different demographic groups in higher education.

Why are both SPSS and machine learning used to analyze the data?

Traditional statistical methods are used to test quantitative relationships, while machine learning models are applied to assess their value for interpreting research findings and identifying opportunities and challenges in the analysis of student attitudes.

Aug 3, 2026

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