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Extended reality for education: Mapping current trends, challenges, and applications Samala, Agariadne Dwinggo; Bojic, Ljubisa; Rawas, Soha; Howard, Natalie-Jane; Arif, Yunifa Miftachul; Tsoy, Dana; Coelho, Diogo Pereira
Jurnal Pendidikan Teknologi Kejuruan Vol 7 No 3 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v7i3.37623

The advancements in 5G technology and Artificial Intelligence (AI) have accelerated the integration of immersive technologies such as Extended Reality (XR) into educational practices. There is a notable scarcity of studies focusing specifically on the applications and impact of XR in academic settings. Most existing research has concentrated on AR and VR, leaving a gap in understanding the full potential of XR. Addressing these gaps and challenges is crucial for harnessing the full potential of XR in education. This study aims to map and analyze the applications, trends, and educational challenges of XR technology. This study conducts a bibliometric analysis covering XR's application in education from 2018 to 2023, analyzing 32 articles from Scopus sources. Key findings highlight XR's annual growth in research publications, with significant contributions from the United States, China, and Canada. XR enriches education by facilitating immersive simulations, real time interaction with virtual objects, and spatial manipulation in three dimensions. It fosters presence and embodiment in virtual environments, supports practical training through realistic simulations, enhances multi-sensory engagement, promotes collaborative learning environments, and improves accessibility for diverse learners. The main challenges of XR technology include high costs, technical hurdles, regulatory issues, infrastructure limitations, and the need for digital literacy and skills. Addressing these challenges, collaborative efforts among educators, researchers, and industry stakeholders are required. Such collaboration is crucial for harnessing the full potential of XR technology to revolutionize education and prepare learners for a dynamic future.

Bias in artificial intelligence: smart solutions for detection, mitigation, and ethical strategies in real-world applications Samala, Agariadne Dwinggo; Rawas, Soha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp32-43

Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.

ChatGPT: a bibliometric analysis and visualization of emerging educational trends, challenges, and applications Samala, Agariadne Dwinggo; Sokolova, Elizaveta Vitalievna; Grassini, Simone; Rawas, Soha
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i4.28119

This study conducts a comprehensive bibliometric analysis and visual exploration of the chat generative pre-trained transformer (ChatGPT) literature in 2023, focusing on its trends, challenges, and applications in education. Using RStudio for bibliometric analysis and VOS viewer for data visualization, this study examines publications from the Scopus database. Following the preferred reporting items for systematic reviews and metaanalyses (PRISMA) guidelines, the systematic review process reinforces the robustness of the analysis. The finding reveals notable trends in the utilization of ChatGPT. Key insights underscore ChatGPT’s increasing role in enhancing engagement, facilitating personalized learning, and fostering student creativity and critical thinking. However, its integration into education encounters obstacles, including ethical considerations, issues of academic honesty, and the imperative for precise usage guidelines; notable applications of ChatGPT encompass language learning, tutoring, automated feedback provision, and functioning as a virtual assistant. These applications showcase ChatGPT’s potential to reshape the educational landscape by introducing innovative pedagogical methods and enriching the student experience. This combined bibliometric and visual analysis provides a comprehensive view of the current status of ChatGPT within the educational domain. It provides a snapshot of the role of ChatGPT in education, offering valuable insights for future research endeavors.

Extended reality for education: Mapping current trends, challenges, and applications Samala, Agariadne Dwinggo; Bojic, Ljubisa; Rawas, Soha; Howard, Natalie-Jane; Arif, Yunifa Miftachul; Tsoy, Dana; Coelho, Diogo Pereira
Jurnal Pendidikan Teknologi Kejuruan Vol 7 No 3 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v7i3.37623

The advancements in 5G technology and Artificial Intelligence (AI) have accelerated the integration of immersive technologies such as Extended Reality (XR) into educational practices. There is a notable scarcity of studies focusing specifically on the applications and impact of XR in academic settings. Most existing research has concentrated on AR and VR, leaving a gap in understanding the full potential of XR. Addressing these gaps and challenges is crucial for harnessing the full potential of XR in education. This study aims to map and analyze the applications, trends, and educational challenges of XR technology. This study conducts a bibliometric analysis covering XR's application in education from 2018 to 2023, analyzing 32 articles from Scopus sources. Key findings highlight XR's annual growth in research publications, with significant contributions from the United States, China, and Canada. XR enriches education by facilitating immersive simulations, real time interaction with virtual objects, and spatial manipulation in three dimensions. It fosters presence and embodiment in virtual environments, supports practical training through realistic simulations, enhances multi-sensory engagement, promotes collaborative learning environments, and improves accessibility for diverse learners. The main challenges of XR technology include high costs, technical hurdles, regulatory issues, infrastructure limitations, and the need for digital literacy and skills. Addressing these challenges, collaborative efforts among educators, researchers, and industry stakeholders are required. Such collaboration is crucial for harnessing the full potential of XR technology to revolutionize education and prepare learners for a dynamic future.

Enhancing cloud resource management: leveraging adversarial reinforcement learning for resilient optimization Dwinggo Samala, Agariadne; Rawas, Soha; Criollo-C, Santiago
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10636

This paper introduces the first adversarial reinforcement learning (ARL) framework for resilient cloud resource optimization under dynamic and adversarial conditions. While traditional reinforcement learning (RL) methods improve adaptability, they fail when faced with sudden workload surges, security threats, or system failures. To address this, we propose an ARL-based approach that trains RL agents using simulated adversarial perturbations, such as workload spikes and resource drops, enabling them to develop robust allocation policies. The framework is evaluated using synthetic and real-world Google Cluster traces within an OpenAI Gym-based simulator. Results show that the ARL model achieves 82% resource utilization and a 180 ms response time under adversarial scenarios, outperforming static policies and conventional RL by up to 12% in terms of cost-effectiveness. Statistical validation (p0.05) confirms significant improvements in resilience. This work demonstrates the potential of ARL for self-healing cloud schedulers in production environments.

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