NAVIGATING THE POST-GEN AI ACADEMIC LANDSCAPE: EFL STUDENTS’ FUNCTIONAL DIFFERENTIATION AND CRITICAL ENGAGEMENT WITH NMT AND CHATGPT - Ummi Nur Laila Sulistyani; Athifah Utami (Lire Journal (Journal of Linguistics and Literature), 2026)
The rapid expansion of Artificial Intelligence (AI) has normalized digital translation in higher education, yet its integration into English as a Foreign Language (EFL) pedagogy remains complicated by an institutional vacuum. Grounded in the Technology Acceptance Model and Cognitive Load Theory, this study examines university students’ multi-tool strategies and critical engagement with Neural Machine Translation (NMT) and Generative AI (GenAI) platforms. Employing a convergent parallel mixed-methods design, quantitative data were gathered from 165 EFL undergraduates (73.7% female, 72.2% sophomores, 61.1% daily active users) via questionnaires, alongside semi-structured interviews with a purposive sub-sample (n=10).The findings reveal an advanced ecosystem characterized by functional differentiation: students strategically leverage specialized NMT engines like DeepL (38.9%) for academic precision but pivot to GenAI tools like ChatGPT (38.9%) for iterative conversational support. While students report high perceived usefulness (98.8%) and ease of use (96.1%) to minimize extraneous cognitive load, they reject passive consumption. Instead, 94.8% actively engage in critical post-editing due to systemic accuracy limitations regarding cultural nuances. Ethically, although 94.7% view automated assistance as an acceptable cognitive scaffold, the absence of clear institutional rules fosters a transparency gap, causing disclosure anxiety. This study dismantles the traditional narrative of student passivity and argues that higher education must move away from obsolete restrictions toward a framework of guided integration and structured translation literacy.