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Political Language in Zohran Mamdani’s Victory Speech: A Critical Discourse Analysis Muhammad Nurfazri; Afita Nur Hayati; Sajidin Sajidin; Dedi Sulaeman; Vini Rizki Nurodiah; Anis Khoirunnisa Syahrussalamah; Hanifa Mawadah; Nabiel Azhari Husaeni
Jurnal Perspektif Vol 10 No 1 (2026): Jurnal Perspektif
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jp.v10i1.425

Abstract

This study examines how progressive political discourse articulates ideology, constructs collective identity, and negotiates power relations between the public and political elites within the context of contemporary democracy. Using a critical qualitative design, the research was grounded in Fairclough’s critical discourse analysis framework: micro, mezzo, and macro. The data were taken from Zohran Mamdani’s victory speech published by the official ABC News YouTube channel. At the micro level, Mamdani frames the electoral victory as the result of a collective struggle through lexical, modal, metaphorical, rhetorical, and agency constructions that delegitimize elites and normalize demands for structural change. At the mezzo level, the speech reconstructs the victory speech genre by linking it to a narrative of ongoing struggle through historical intertextuality and media distribution strategies that position the audience as active political subjects. At the macro level, the discourse operates as an ideological intervention that challenges neoliberal and oligarchic hegemony by asserting democratic socialism as a response to inequality in contemporary democracy. Hence, the study concludes that critical discourse analysis provides a robust analytical lens for uncovering the role of language in both reproducing and contesting power relations, thereby contributing to a more critical understanding of political discourse in democratic contexts.
Human–AI Collaboration in EFL Writing: A Conceptual Framework for Intelligent Feedback Systems Vini Rizki Nurodiah; Titin Nurjanah; Andang Saehu; Cipto Wardoyo
JEPAL (Journal of English Pedagogy and Applied Linguistics) Vol. 7 No. 1 (2026): July 2026
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/jepal.v7i1.2041

Abstract

Artificial intelligence (AI) has increasingly been integrated into English as a Foreign Language (EFL) writing instruction, offering new opportunities to improve students’ writing through automated and intelligent feedback. Nevertheless, existing studies remain fragmented and predominantly emphasize the technological capabilities of AI tools rather than their pedagogical integration within classroom practice. This study aims to synthesize recent literature and propose a conceptual framework for human–AI collaboration in EFL writing by integrating technological, pedagogical, and learner-centered perspectives. Employing a qualitative library research design, the study systematically reviewed peer-reviewed journal articles published between 2016 and 2026. Data were collected from Google Scholar, Scopus, ERIC, and ScienceDirect and analyzed using thematic analysis involving coding, categorization, and theme development. The findings reveal three interrelated components of effective AI-assisted writing instruction: AI as an intelligent feedback provider, teachers as pedagogical scaffolding agents, and students as self-regulated learners. These components interact dynamically to support writing development, strengthen learner autonomy, and promote meaningful engagement with feedback. The review also identifies several pedagogical challenges, including learners’ overdependence on AI-generated feedback and limited critical engagement with automated suggestions. The study concludes that effective AI integration in EFL writing requires a balanced collaboration between technological support and pedagogical guidance, grounded in constructivist learning theory, self-regulated learning, and feedback literacy. The proposed conceptual framework contributes to the theoretical foundation of AI-assisted language learning and provides practical implications for integrating intelligent feedback systems into EFL writing instruction.