Journal of Smart Education and Emerging Technology
Vol 1 No 1 (2025) : July

Examining Digital Security Awareness and Psychological Factors in Predicting Digital Technology Use Among Generation Z

Muh. Alief Rezky Anisar (Universitas Negeri Makassar)
Siti Fatimah Azzahro (Universitas Negeri Makassar)



Article Info

Publish Date
21 Jul 2025

Abstract

Background/Context: The rapid growth of information technology has shaped the behavior and mental health of Generation Z, a group deeply connected to digital platforms. This development provides many opportunities for communication and learning but also introduces significant risks such as identity theft, online fraud, and cyberbullying, which may increase their psychological vulnerability.Objective/Purpose: The purpose of this study is to analyze the influence of digital security awareness and psychological factors on technology use among Generation Z, as well as its implications for their mental well-being.Method: A quantitative research method was applied with 106 respondents from Universitas Negeri Makassar. Data were collected through questionnaires covering aspects of personal data protection, safe online interaction, and healthy internet use.Results: The results show that although Generation Z exhibits a relatively high awareness of digital security, excessive use of technology, particularly during the pandemic period, has led to increased levels of anxiety and feelings of insecurity.Conclusion: This study concludes that digital self-regulation is a key factor in maintaining mental health when engaging with technology. Support from parents, educators, and policymakers is essential to guide young generations in managing digital risks effectively and ensuring that technological benefits are maximized without compromising psychological well-being.

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Journal Info

Abbrev

JSEET

Publisher

Subject

Computer Science & IT Education

Description

Artificial Intelligence in Education (AIED), exploring intelligent and adaptive educational applications that support learning and teaching. Machine Learning in Education, focusing on predictive and adaptive models for learning support, personalization, and educational decision-making. Deep Learning ...