<![CDATA[The rapid adoption of generative artificial intelligence (AI) tools such as ChatGPT, Grammarly, QuillBot, and machine translation has reshaped how English is learned in higher education. Working students, who must balance employment and study under severe time constraints, represent a learner group whose needs differ from those of conventional full-time students, yet they remain underexplored in the literature. This article is a conceptual review that synthesises current scholarship to examine the potential and the risks of AI-assisted English learning for working students. Using a narrative literature review of studies published between 2018 and 2024, the discussion is organised around five dimensions that mirror common acceptance frameworks: perceived usefulness, perceived ease of use, learning effectiveness, learner concerns, and the working-student context. Drawing on the Technology Acceptance Model, self-regulated learning theory, and andragogy, the article argues that AI offers meaningful affordances (instant feedback, personalisation, flexibility, anxiety-free practice) that align well with the fragmented schedules of working learners, while also posing risks of over-reliance, inaccurate output, loss of cultural and human interaction, and data-privacy concerns. The article proposes a conceptual framework and five propositions positioning AI as a complement to, rather than a substitute for, the teacher, and recommends a blended human-AI model built on micro-learning, workplace-relevant content, and critical AI literacy. Implications for institutions, lecturers, and learners, together with directions for future empirical testing, are discussed.]]>
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