Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Journal of Digitalization in Physics Education (JDPE) Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Riski Ramadani
State University of Surabaya
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Physics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
From Algorithms to Awareness: AI-Enhanced Physics Education in the Framework of Education for Sustainable Development Hanan Zaki Alhusni; Binar Kurnia Prahani; Titin Sunarti; Madlazim Madlazim; Riski Ramadani; Muhammad Rey Dafa Ahmadi
Journal of Current Studies in SDGs Vol. 1 No. 3 (2025): September
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.1.3.83

Abstract

Objective: This study synthesizes research on the integration of Artificial Intelligence (AI) in physics education within the framework of Education for Sustainable Development (ESD). It aims to map current trends, highlight educational opportunities, and identify research gaps regarding AI’s potential to enhance learning outcomes and foster sustainability competencies. Method: A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines. A total of 48 peer-reviewed studies published between 2015 and 2025 were collected from major academic databases and Google Scholar using Boolean search strings combining terms related to AI, physics education, and ESD. The data were analyzed thematically to identify recurring patterns in AI technologies, physics content areas, ESD dimensions, methodologies, and educational outcomes. Results: The findings indicate that machine learning, deep learning, intelligent tutoring systems, and AI-powered virtual laboratories are the most common applications in physics education. These technologies were primarily applied in mechanics, electricity, and energy-related topics, with limited studies focusing on environmental physics. While AI consistently improved motivation, achievement, and critical thinking, the integration of broader ESD competencies remained uneven, with environmental literacy, social responsibility, and ethical reasoning less frequently addressed. Novelty: This study contributes by linking AI, physics education, and ESD, which are often studied separately, and proposes a conceptual roadmap to align AI integration with sustainable education goals.
From Algorithms to Awareness: AI-Enhanced Physics Education in the Framework of Education for Sustainable Development Hanan Zaki Alhusni; Binar Kurnia Prahani; Titin Sunarti; Madlazim Madlazim; Riski Ramadani; Muhammad Rey Dafa Ahmadi
Journal of Current Studies in SDGs Vol. 1 No. 3 (2025): September
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.1.3.83

Abstract

Objective: This study synthesizes research on the integration of Artificial Intelligence (AI) in physics education within the framework of Education for Sustainable Development (ESD). It aims to map current trends, highlight educational opportunities, and identify research gaps regarding AI’s potential to enhance learning outcomes and foster sustainability competencies. Method: A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines. A total of 48 peer-reviewed studies published between 2015 and 2025 were collected from major academic databases and Google Scholar using Boolean search strings combining terms related to AI, physics education, and ESD. The data were analyzed thematically to identify recurring patterns in AI technologies, physics content areas, ESD dimensions, methodologies, and educational outcomes. Results: The findings indicate that machine learning, deep learning, intelligent tutoring systems, and AI-powered virtual laboratories are the most common applications in physics education. These technologies were primarily applied in mechanics, electricity, and energy-related topics, with limited studies focusing on environmental physics. While AI consistently improved motivation, achievement, and critical thinking, the integration of broader ESD competencies remained uneven, with environmental literacy, social responsibility, and ethical reasoning less frequently addressed. Novelty: This study contributes by linking AI, physics education, and ESD, which are often studied separately, and proposes a conceptual roadmap to align AI integration with sustainable education goals.
Exploratory Factor Analysis (EFA) of PhET Simulation-Based Innovation in Rigid Body Equilibrium Learning to Enhance Students’ Conceptual Understanding Hanan Zaki Alhusni; Ratna Purnamawati; Binar Kurnia Prahani; Titin Sunarti; Riski Ramadani; Iqbal Ainur Rizki
Journal of Digitalization in Physics Education Vol. 1 No. 3 (2025): December
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jdpe.v1i3.42475

Abstract

Objective: This study aimed to analyze the validity and reliability of an instrument developed to measure students’ perceptions of PhET simulation–based innovations in learning rigid body equilibrium. The research sought to determine whether the instrument accurately captured students’ conceptual understanding, engagement, and satisfaction while also identifying latent factors underlying their responses through Exploratory Factor Analysis (EFA). Method: The study employed a quantitative survey approach with 25 Likert-scale items distributed to students in one secondary school. Data were analyzed using descriptive statistics, Kaiser-Meyer-Olkin (KMO), and Bartlett's test for sampling adequacy, followed by EFA with Varimax rotation. Reliability testing was conducted through Cronbach's Alpha to evaluate the internal consistency of extracted factors. Results:  The findings revealed that the instrument achieved strong overall reliability (Cronbach’s α = .909). EFA identified nine distinct factors, extending beyond the original five theoretical dimensions of ease of use, engagement, conceptual clarity, collaboration, and satisfaction. While the first five factors demonstrated high reliability (α > .75), Factors 6–9 exhibited weaker reliability, indicating the need for refinement. These results confirm that PhET simulations effectively enhance conceptual understanding and engagement, but also reveal additional dimensions of students’ learning experiences. Novelty: This study contributes to physics education research by providing a validated multidimensional instrument for evaluating PhET-based learning in rigid body equilibrium. The emergence of nine empirical factors highlights the complex nature of students’ perceptions and underscores the need for more nuanced measurement tools. Unlike prior research focusing solely on conceptual gains, this study emphasizes the psychometric validation of students’ experiences, offering a novel framework for assessing technology integration in physics education.
Co-Authors Binar Kurnia Prahani Hanan Zaki Alhusni Hanan Zaki Alhusni Iqbal Ainur Rizki Madlazim Madlazim Muhammad Rey Dafa Ahmadi Ratna Purnamawati Titin Sunarti