Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
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All Journal International Journal of Educational Technology and Society Educational Dynamics: International Journal of Education and Social Sciences
Danellie C. Llamas
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Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Artificial Intelligence and Education: Examining Ethical Awareness and Social Responsibility in Classroom Applications Agustini; Danellie C. Llamas
Educational Dynamics: International Journal of Education and Social Sciences Vol. 2 No. 4 (2025): Educational Dynamics: International Journal of Education and Social Sciences
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/educationaldynamics.v2i4.250

Abstract

Artificial Intelligence (AI) has increasingly become an integral component of modern education, offering significant opportunities to improve accessibility, efficiency, and personalized learning. This study explores students’ perceptions of AI in educational contexts, focusing not only on its practical benefits but also on ethical concerns, particularly regarding data privacy and social responsibility. Adopting a mixed-method design, the research collected data through surveys and classroom observations. The survey responses were analyzed using descriptive statistics to identify trends in perceptions, while observation notes were thematically coded to capture students’ real interactions with AI applications. The findings reveal that students generally exhibit a positive attitude toward AI, recognizing its potential to enhance learning experiences by making education more accessible and efficient. However, the study also highlights that concerns over data privacy and the potential misuse of information remain central issues. Moreover, a notable gap exists between students’ appreciation of the practical benefits of AI and their awareness of ethical dimensions such as algorithmic bias and digital responsibility. This imbalance suggests that while students are ready to embrace AI as a tool, they may not yet be adequately prepared to address the broader ethical implications of its use. The discussion situates these findings within the framework of digital ethics, emphasizing the need for education systems to integrate ethical literacy alongside technological skills. By doing so, schools and universities can ensure that students are both technologically competent and socially responsible. The study concludes that although AI is welcomed in educational settings, greater emphasis on digital ethics education is essential to maximize its benefits and minimize potential risks. This research provides a basis for future studies on policy development and ethical practices for AI in the classroom.
Adaptive Learning Analytics for Tracking Student Performance and Predicting Academic Success in Digital Classrooms Sri Suharti; Imelda Hutabarat; Danellie C. Llamas
International Journal of Educational Technology and Society Vol. 1 No. 3 (2024): September : International Journal of Educational Technology and Society
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijets.v1i3.411

Abstract

This research focuses on the application of predictive analytics in digital classrooms to track and predict student performance. The study aims to address the limitations of traditional teacher judgment, which often relies on limited data points and subjective assessments. The research proposes a machine learning-driven approach that utilizes data from Learning Management Systems (LMS), including student engagement, academic performance, and attendance, to predict student success or failure with greater accuracy. Various machine learning techniques, such as Support Vector Machine (SVM) and Random Forest (RF), are applied to develop a predictive model that can identify at-risk students early. The findings show that the model achieves an accuracy rate of over 85%, with key predictors including past academic performance and student engagement. This model outperforms traditional assessment methods by providing real-time, data-driven insights that enable timely interventions. The study concludes that predictive analytics has significant potential to enhance educational outcomes by offering personalized support and improving curriculum design. However, challenges such as data integration, fairness, and privacy concerns must be addressed for broader implementation.
Co-Authors Agustini Imelda Hutabarat Suharti, Sri