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English speaking skill is a crucial competence for university students, particularly those involved in student organizations focusing on language development. Members of English clubs are expected to actively use English in academic and organizational contexts; however, many still face challenges related to pronunciation accuracy, fluency, limited active vocabulary, and low confidence. These challenges are often caused by limited structured speaking practice and insufficient integration between digital learning tools and communicative pedagogy.This community service program aimed to enhance the English-speaking skills of members of the Bhamada English Club at Universitas Bhamada Slawi through the utilization of the Duolingo application integrated with a Task-Based Learning (TBL) approach. The program was implemented through three stages: preparation, implementation, and evaluation. Activities included needs analysis, speaking pre-test, guided Duolingo practice focusing on pronunciation and speaking features, task-based speaking activities, and post-test evaluation.The results demonstrated improvements in pronunciation accuracy, speaking fluency, and participants’ confidence in using English orally. The integration of Duolingo as a source of comprehensible input and Task-Based Learning as a communicative output strategy proved effective in enhancing speaking skills. This program indicates that technology-supported task-based instruction can serve as an effective and sustainable model for improving English speaking skills among university students.

The development of Artificial Intelligence (AI) technology has significantly changed strategy and decision making in marketing management. However, the massive use of AI raises new challenges regarding ethics, transparency and governance. This research aims to analyze the impact of using AI, especially recommender systems and large language models (LLMs), on the effectiveness of marketing decisions, as well as the role of AI governance in controlling emerging ethical issues. The research method uses a quantitative approach with Structural Equation Modeling (SEM) analysis of data collected from 250 marketing professionals in Indonesia. The research results show that the use of AI has a significant positive effect on the effectiveness of marketing decisions (β=0.62, p<0.001), but also raises ethical issues (β=0.48, p<0.01). Ethical issues were proven to reduce the effectiveness of marketing decisions (β=-0.31, p<0.05), while good AI governance was able to moderate the negative impact of ethical issues (β=0.27, p<0.05). These findings underscore the importance of AI governance in building effective and ethical marketing systems.

Personal financial management behavior has gained prominence amid economic globalization, digital transformation, and crisis-induced shifts that reshape individual decision-making, budgeting, saving, and risk practices. This study conducts a comprehensive bibliometric analysis to chart the intellectual structure, growth patterns, and future orientations of research in this domain. Drawing on 312 English-language publications from the Scopus database spanning 2000 to 2024, the analysis employs VOSviewer for co-authorship, keyword co-occurrence, and co-citation mapping, complemented by performance metrics on trends and productivity. Findings reveal a marked acceleration in scholarly output, particularly after 2020, driven by heightened attention to digital tools and resilience factors. Thematic clusters highlight progression from foundational literacy and demographic influences to psychological mediators (e.g., self-efficacy, attitudes) and outcomes centered on well-being and socialization. Geographic contributions concentrate in the United States and Indonesia, with strong Asia-Pacific networks, while productive authors form specialized collaborative hubs. The intellectual base integrates behavioral frameworks with empirical applications, underscoring interdisciplinary depth. These insights address fragmentation in prior work by providing a unified knowledge map, revealing gaps in cross-cultural integration and dynamic digital modeling. Implications extend to guiding targeted interventions for financial education and policy, fostering individual resilience in volatile environments. This synthesis supports scholars and practitioners in advancing evidence-based approaches to sustainable personal finance practices.

The use of Large Language Models (LLMs) in scientific research is becoming increasingly widespread, but presents epistemic risks that are not yet fully understood. This article discusses how the probabilistic mechanisms of LLM can produce outputs that appear correct and justified but are actually dependent on epistemic luck, thus resembling the Gettier case pattern. Through a conceptual study approach, this research clarifies concepts, analytically reconstructs the generative structure of LLM, and conducts a normative analysis of its implications for scientific accountability and authorship. The results of the analysis show that Algorithmic Gettier Cases (AGCs) occur when linguistic coherence deceives users and creates the impression of justification, even though the truth that emerges is statistical coincidence and is not supported by valid causal relationships. This condition poses a serious challenge to the attribution of knowledge and author responsibility in the production of academic texts. To address this issue, this article proposes the principle of Hyper-Justification Obligation, which is the ethical obligation for researchers to actively verify and causally reason every AI output before using it in scientific works. This research provides a theoretical contribution to understanding the epistemic risks of LLM and offers an ethical foundation for academic practice in the era of generative AI.