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All Journal Al-Hijr: Journal of Adulearn World Journal International of Lingua and Technology Journal Neosantara Hybrid Learning Journal of Tecnologia Quantica Research of Scientia Naturalis
Vann, Rithy
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Education Languange, Linguistic, Communication & Media Physics Social Sciences Other

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The Role of Chatgpt as A Virtual Assistant in Increasing Student Learning Collaboration Dara, Chenda; Vann, Rithy; Sok, Vann
Al-Hijr: Journal of Adulearn World Vol. 3 No. 4 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v3i4.847

Abstract

The rapid development of artificial intelligence (AI) has led to the integration of AI tools like ChatGPT into various sectors, including education. One of the most promising applications is the use of AI as a virtual assistant to enhance student collaboration and learning experiences. Traditional education methods often struggle to foster effective collaboration among students, particularly in large, diverse classrooms. This study aims to explore the role of ChatGPT as a virtual assistant in increasing student collaboration and enhancing the learning process. The primary objective is to evaluate how ChatGPT can assist students in collaborative learning tasks, promote peer interaction, and provide personalized support. A mixed-methods research design was employed, combining qualitative and quantitative data collection methods. The study involved 150 students from different academic disciplines, who were introduced to ChatGPT as a collaborative tool for group projects. Data was collected through surveys, interviews, and collaboration performance assessments. The results indicate that ChatGPT significantly improved student collaboration, with students reporting increased interaction, engagement, and productivity during group tasks. Additionally, ChatGPT’s personalized assistance helped students overcome knowledge gaps and foster a collaborative learning environment. This study concludes that ChatGPT can be an effective virtual assistant in enhancing student collaboration and improving overall learning outcomes, offering valuable insights into AI’s potential in education.
Community-Based Social Education for Sustainable Development – An Indonesian Perspective on Collaborative Learning Models Vann, Rithy; Rith, Vicheka; Suyitno, Suyitno
Journal Neosantara Hybrid Learning Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v3i1.2174

Abstract

Community-based social education plays a crucial role in fostering sustainable development, especially in countries with diverse social structures such as Indonesia. However, the implementation of collaborative learning models within this framework remains underexplored. This study aims to investigate the effectiveness of community-based social education in supporting sustainable development through the application of collaborative learning models from an Indonesian perspective. Employing a qualitative research method with a case study approach, data were collected through interviews, focus group discussions, and observations involving educators, community leaders, and learners in selected rural and urban communities. The findings reveal that collaborative learning models significantly enhance community engagement, improve critical thinking skills, and promote shared responsibility among participants. Furthermore, the integration of local wisdom and cultural values into learning processes strengthens the relevance and sustainability of educational programs. The study concludes that community-based social education, when supported by well-structured collaborative learning models, can serve as an effective strategy for achieving sustainable development goals. It emphasizes the need for policy support, capacity building, and continuous evaluation to ensure the scalability and impact of such educational initiatives.  
The Ethics of AI in Education: An Empirical Study on Students’ and Teachers’ Attitudes toward Responsible AI Use Nofirman, Nofirman; Vann, Rithy; Sefrizal, Levy; Dara, Ravi
Al-Hijr: Journal of Adulearn World Vol. 4 No. 2 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v4i2.983

Abstract

The rapid integration of artificial intelligence (AI) into educational settings has outpaced the development of clear ethical guidelines, creating significant challenges related to academic integrity, data privacy, and algorithmic bias. Understanding stakeholder attitudes is crucial for developing effective policies for responsible AI use. This study aimed to empirically investigate and compare the attitudes of high school students and teachers regarding the ethical challenges and responsible use of AI in education. A mixed-methods approach was employed, using a quantitative survey administered to 250 students and 50 teachers to gauge attitudes toward AI ethics. This was supplemented by qualitative data from semi-structured focus groups to explore the reasoning behind these attitudes. The findings revealed a significant divergence in attitudes, with teachers expressing greater concern for academic integrity and data privacy, while students were more focused on AI as a tool for efficiency. Both groups, however, demonstrated a shared desire for clear, explicit ethical guidelines. A notable gap exists between student and teacher perspectives on AI ethics in education. To foster a culture of responsible AI use, educational institutions must implement comprehensive training and develop clear policies that address the distinct concerns of both stakeholder groups.
Blended Learning Approaches in Language Instruction: A Comparative Study Dara, Sokha; Vann, Rithy; Pitri, Alisyah; Nilfatri, Nilfatri
Journal International of Lingua and Technology Vol. 3 No. 3 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiltech.v3i3.777

Abstract

Blended learning, which combines traditional face-to-face instruction with online learning, has gained significant attention in language education due to its potential to enhance flexibility and engagement. Despite its growing popularity, there is limited comparative research on the effectiveness of different blended learning models in language instruction. This study addresses this gap by examining the impact of various blended learning approaches on language learning outcomes. The research aims to compare the effectiveness of different blended learning models in language instruction, focusing on their impact on learner engagement, language proficiency, and satisfaction. It seeks to identify best practices for integrating online and face-to-face components to optimize language learning experiences. A mixed-methods approach was employed, involving 200 language learners divided into three groups, each experiencing a different blended learning model. Data were collected through pre- and post-tests, surveys, and focus group discussions. Quantitative data were analyzed using statistical software, while qualitative data were subjected to thematic analysis. The findings revealed that the flipped classroom model significantly improved learner engagement and language proficiency compared to other models. Participants in the flipped classroom group reported higher satisfaction levels and greater confidence in using the target language. However, all blended learning models showed positive outcomes, suggesting that the integration of online and face-to-face components enhances language learning.
Nanostructured Catalysts for Efficient Energy Conversion: Recent Advances Vann, Rithy; Dara, Ravi; Sok, Vann
Research of Scientia Naturalis Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i4.1573

Abstract

The global transition towards sustainable energy sources has driven significant research into developing advanced catalytic materials that can enable efficient energy conversion processes. Nanostructured catalysts, with their unique physiochemical properties, have emerged as promising candidates to address the challenges associated with energy conversion technologies, such as low conversion efficiencies and high production costs. Understanding the recent advancements in the field of nanostructured catalysts is crucial for accelerating the development of next-generation energy conversion systems. This review article aims to provide a comprehensive overview of the recent progress in the design, synthesis, and application of nanostructured catalysts for efficient energy conversion. The study investigates the underlying principles governing the enhanced catalytic performance of nanomaterials and examines their potential impact on diverse energy conversion processes, including fuel cells, water splitting, and photocatalytic systems. The research methodology involves an extensive literature review of peer-reviewed journal articles, conference proceedings, and patent documents published within the last five years. The analysis focuses on the latest developments in the synthesis and characterization of nanostructured catalysts, as well as their performance evaluation under realistic operating conditions. The review highlights the successful implementation of various nanostructured catalyst architectures, such as nanoparticles, nanotubes, nanosheets, and core-shell structures, in enhancing the catalytic activity, selectivity, and stability for energy conversion applications. Significant advancements in the rational design of catalysts through the control of composition, morphology, and surface properties are discussed, along with their impact on improving energy conversion efficiencies and reducing production costs. The study concludes that the continued development of nanostructured catalysts holds great promise for addressing the current challenges in energy conversion technologies. The insights gained from this review can guide future research directions and facilitate the translation of nanostructured catalyst innovations into practical, large-scale energy conversion systems.
Quantum Sensing of Weak Magnetic Fields using Diamond NV Centers in Biological Environments Vann, Rithy; Raza, Amir; Zulu, Nomsa
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3581

Abstract

Quantum sensing using nitrogen-vacancy (NV) centers in diamond has emerged as a powerful approach for detecting extremely weak magnetic fields with high spatial resolution and ambient operational conditions. Despite their proven sensitivity in controlled environments, the performance of NV-based sensors in biological systems remains challenged by decoherence, optical scattering, and environmental noise. This study aims to investigate the capability of diamond NV centers to detect weak magnetic fields in biologically relevant environments and to evaluate the factors influencing their performance. An experimental–computational approach was employed, combining optical detection of magnetic resonance (ODMR) measurements with simulations of spin dynamics under varying environmental conditions. Nanodiamond samples were tested across buffer solutions, cell culture media, and tissue-like environments. The results indicate that NV centers retain the ability to detect weak magnetic fields in biological settings, although sensitivity decreases due to reduced coherence time and optical contrast. Surface functionalization improves stability and partially mitigates environmental effects, enhancing overall sensor performance. These findings suggest that NV-based quantum sensors offer a promising platform for non-invasive biological magnetometry, provided that material engineering and noise mitigation strategies are optimized. This study concludes that integrating quantum sensing with biological systems is feasible and can advance applications in biomedical diagnostics and cellular imaging..
Co-Authors Dara, Chenda Dara, Ravi Dara, Sokha Nilfatri, Nilfatri Nofirman, Nofirman Pitri, Alisyah Raza, Amir Rith, Vicheka Sefrizal, Levy Sok, Vann Suyitno Suyitno Zulu, Nomsa