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All Journal Journal International of Lingua and Technology Journal Markcount Finance Lingeduca: Journal of Language and Education Studies International Journal of Educational Narratives Journal of Social Science Utilizing Technology Journal of Social Science Utilizing Technology
Rahman, Rashid
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Journal of Social Science Utilizing Technology

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Comparative Analysis of e-Learning and u-Learning Environments in Corporate Training Febriyantoro, Mohamad Trio; Ikhlas, Rifki Zaitul; Rahman, Rashid; Rahman, Shahinur
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1737

Abstract

Background. The rapid evolution of digital technologies has significantly impacted corporate training methods. Traditional e-learning environments have been widely adopted, yet the emergence of ubiquitous learning (u-learning) presents a shift towards more flexible, context-aware learning experiences. Despite the growing interest, limited studies provide a comparative analysis between e-learning and u-learning in corporate settings. Purpose. This research aims to evaluate and compare the effectiveness of e-learning and u-learning environments in corporate training, focusing on learner engagement, content delivery, and overall performance. Method. A mixed-methods approach was used, combining quantitative surveys and qualitative interviews with corporate employees who participated in both e-learning and u-learning training programs. Data were collected across several multinational companies, and analyzed using statistical tools to identify performance trends and engagement metrics. Results. Findings reveal that u-learning environments enhance learner engagement and adaptability due to their flexibility in accessing content across diverse devices and contexts. Conversely, e-learning showed better outcomes in structured, course-driven scenarios but lacked the same level of interaction and contextual learning. Conclusion. The study concludes that u-learning environments provide a more personalized and engaging training experience, particularly for employees with diverse learning needs. Organizations should consider integrating u-learning strategies alongside traditional e-learning for more dynamic corporate training programs.
AI and Social Equity: Challenges and Opportunities in the Age of Automation Judijanto, Loso; Mudinillah, Adam; Rahman, Rashid; Joshi, Nikhil
Journal of Social Science Utilizing Technology Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i1.2117

Abstract

The rapid advancement of Artificial Intelligence (AI) and automation technologies has transformed various aspects of modern life, from labor markets to public services. While AI offers potential for innovation and efficiency, it also raises significant concerns regarding social equity, especially for marginalized and underrepresented communities. These concerns highlight the need for a critical examination of how AI systems may reinforce or mitigate existing societal disparities. This study aims to explore the challenges and opportunities that AI poses to social equity in the age of automation. The research focuses on identifying potential biases in AI-driven decision-making processes and assessing the impact of automation on employment, education, and access to services. Using a mixed-methods approach, the study combines qualitative interviews with stakeholders from policy, tech industry, and affected communities, alongside quantitative analysis of labor and demographic data. This methodological design allows for a comprehensive understanding of both structural and experiential dimensions of AI’s impact. The findings reveal that while AI has the potential to improve service delivery and expand access to information, its deployment often reflects and amplifies existing inequalities when ethical and inclusive frameworks are absent. Particularly in automated hiring systems and predictive policing, biases embedded in algorithms disproportionately affect vulnerable groups. The study concludes that addressing AI's social equity implications requires intentional design, inclusive policy, and sustained public engagement. As automation continues to reshape society, equity must become a central consideration in AI development and governance.
Co-Authors Anis, Nina Farah, Rina Fariq, Aiman ika agustina Ikhlas, Rifki Zaitul Joshi, Nikhil Judijanto, Loso Mudinillah, Adam Muh Syafei Nita Rosmala Dewi Nizam, Zain Perlambang, Rizal Rahman, Shahinur Sun Wei Yoga Anjas Pratama Zaki, Amin