Nurul Huda
Universiti Brunei Darussalam

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

HEALTH INEQUALITY AND SOCIAL DETERMINANTS: SOCIOLOGICAL PERSPECTIVES ON PUBLIC HEALTH POLICY wijaya wijaya; Hassan Ali; Nurul Huda
Cognitionis Civitatis et Politicae Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/politicae.v3i2.4184

Abstract

Health inequality remains a persistent global challenge, influenced significantly by social determinants such as income, education, and access to healthcare. Sociological perspectives emphasize that these disparities are not merely the result of individual behaviors but are deeply embedded within social structures and systems. This research explores the impact of social determinants on health inequality and examines how public health policies address these factors. The study aims to assess how sociological insights can inform public health policies aimed at reducing health disparities, particularly in marginalized communities. A qualitative research design was employed, utilizing document analysis, in-depth interviews, and focus group discussions across various socio-economic groups. The findings indicate that socio-economic status and education are the most significant determinants of health outcomes, with individuals from lower-income and less-educated backgrounds facing higher rates of chronic diseases. Additionally, public health policies that integrate these social determinants have been more effective in addressing health inequalities than those focused solely on healthcare access. The study concludes that addressing the root causes of health disparities through sociologically informed policy frameworks is essential for reducing inequality and promoting more equitable health outcomes.
DEMOCRATIZING LANGUAGE PROFICIENCY: THE IMPACT OF GENERATIVE AI ON LEARNER AUTONOMY AND ACADEMIC INTEGRITY IN SECOND LANGUAGE ACQUISITION Delsa Miranty; Hassan Ali; Nurul Huda
Lingeduca: Journal of Language and Education Studies Vol. 5 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/lingeduca.v5i1.3874

Abstract

Generative artificial intelligence has rapidly transformed second language acquisition by providing learners with continuous access to automated feedback, translation support, grammar correction, and interactive language practice. Expansion of AI-assisted learning environments has increased opportunities for independent language development and reduced barriers associated with instructional access and linguistic anxiety. Simultaneous growth of AI integration in education has also generated concerns regarding academic integrity, technological dependency, and the authenticity of learner language production within academic contexts. This study aimed to examine the impact of generative AI on learner autonomy and academic integrity in second language acquisition. Particular attention was directed toward analyzing how AI-assisted language learning influences self-regulated learning behavior, writing performance, language confidence, and ethical awareness among second language learners in higher education environments. A mixed-methods sequential explanatory design was employed involving 300 undergraduate students enrolled in second language learning programs across three universities. Quantitative data were collected through learner autonomy scales, academic integrity perception questionnaires, and AI usage surveys, while qualitative findings were obtained through interviews, classroom observations, and reflective journals. Statistical analysis included ANOVA, regression analysis, and correlation testing to identify relationships between AI usage and educational outcomes. Results demonstrated that generative AI significantly improved learner autonomy, writing performance, and language confidence through personalized linguistic support and immediate feedback. High-frequency AI users, however, exhibited stronger technological dependence and lower academic integrity awareness regarding authentic language production. Findings confirm that generative AI democratizes language learning opportunities while simultaneously requiring balanced pedagogical frameworks supporting ethical engagement, authentic linguistic development, and responsible educational technology integration.