Biography
Biography pending.
Conference contributions 2
GENERATIVE AI IN ALGERIAN EDUCATION:
Rethinking student agency and ethical learning practices
This study explores the impact of Generative Artificial Intelligence on student agency and language learning practices in the Algerian higher education context. It focuses on Algerian EFL learners' attitudes toward the use of generative AI and examines how human–AI collaboration is reshaping educational experiences, learner autonomy, and ethical engagement with digital technologies. The study involves EFL students from different Algerian universities. In Algerian EFL classrooms, language learning is still largely based on traditional teacher-centered approaches, which often limit opportunities for interaction and autonomous learning. Generative AI technologies, however, provide learners with personalized feedback, adaptive learning experiences, and interactive language practice that may enhance engagement and independent learning. At the same time, their growing presence in education raises ethical concerns related to academic integrity, overreliance on AI tools, digital inequality, and responsible use of technology. Using a mixed-methods approach based on semi-structured surveys, this study investigates the extent to which Algerian EFL learners use generative AI for language learning, their perceptions of ethical human–AI collaboration, and the challenges influencing on effective implementation. The findings reveal that although the use of generative AI remains limited due to infrastructural constraints such as weak internet connectivity and insufficient technological resources, EFL students still hold positive perspectives toward its educational potential. The study concludes that generative AI can foster educational transformation in Algeria by promoting learner autonomy, digital literacy, and ethically informed human–AI collaboration in language education.
THE ILLUSION OF NEUTRALITY:
A critical discourse analysis of hedging and ideological positioning in ChatGPT
Large Language Models (LLMs) are increasingly promoted as objective and impartial tools that can provide balanced information across a wide range of topics. Despite growing scholarly attention to algorithmic bias in artificial intelligence, comparatively little research has examined the linguistic mechanisms through which AI-generated discourse constructs an appearance of neutrality. This issue is particularly significant in the case of ChatGPT, whose responses are frequently perceived as unbiased despite concerns regarding the ideological assumptions embedded within its training data and output generation processes. Drawing on Norman Fairclough's three-dimensional model of Critical Discourse Analysis (CDA), this paper investigates the role of hedging as a discursive strategy in the construction of neutrality within ChatGPT responses. The study analyzes a corpus of responses generated from prompts addressing selected controversial social issues, with particular attention to linguistic features such as modality, mitigation, evidentiality, attribution, and evaluative language. Through a systematic examination of expressions such as "some argue," "it may be," and "research suggests," the paper explores how ChatGPT negotiates competing viewpoints while simultaneously positioning certain perspectives as more legitimate, credible, or authoritative than others. The study argues that neutrality in AI-generated discourse should not be understood as the absence of ideology, but rather as a discursively constructed effect achieved through specific linguistic choices. By foregrounding hedging as a central mechanism in the production of apparent objectivity, this research contributes to emerging discussions on language, ideology, and algorithmic meaning-making. More broadly, it demonstrates the relevance of Critical Discourse Analysis for understanding the social and epistemological implications of generative artificial intelligence in contemporary digital communication.