Beyond Memorisation: An AI-Mediated Course Design for Vocabulary Learning in Higher Education

Abstract

English language teaching (ELT) in higher education has undergone successive pedagogical transformations, evolving from rote memorisation to task-based and communicative approaches, integrating computer-assisted language learning (CALL), and now entering an era of artificial intelligence (AI)–mediated learning. This paper argues that AI adoption should not be viewed as a pedagogical rupture, but as a continuation of a long-standing shift toward learner-centred, meaning-focused, and interaction-driven language education. Situating AI within this trajectory, the paper contends that successful integration relies not on technological novelty, but on research-informed pedagogy, ethical awareness, and evidence-based practice. Echoing a longstanding caution against allowing education to be driven by the measurement of outcomes at the expense of purpose, it asks not what is technologically possible, but what is pedagogically meaningful. To illustrate this argument, the paper presents an action research project documenting the ongoing construction of a new elective course on vocabulary development at a research-focused liberal arts university in Hong Kong. Moving beyond rote memorisation, the course enhances vocabulary acquisition through active learning, textual analysis, and extensive reading, employing techniques such as morphological analysis and contextual inference. Drawing on literary and non-literary materials, it adopts a scaffolded design transitioning from guided instruction to independent learning, fostering critical thinking alongside lexical growth. Through discussions, presentations, and written analyzes understood as a recursive, problem-solving process, students apply new vocabulary in academic and real-world contexts—offering a practical model for pedagogically grounded, AI-mediated vocabulary learning.