AI-mediated Self-directed Vocabulary Learning: An Activity Theory Analysis of Mobile English Learning Resource Ecology

Abstract

The current research examines how English as a Foreign Language (EFL) students engage with artificial intelligence (AI) and AI-integrated tools within their Mobile English Learning Resource (MELR) ecologies for self-directed learning (SDL) of vocabulary. Using the Activity Theory (AT) framework, the research examines how AI integration influences students’ vocabulary learning processes and how it may shape their learning activity systems. The study employed a longitudinal qualitative approach and involved 22 purposely selected Vietnamese EFL students who kept learning journals for 7 weeks and then participated in semi-structured interviews and focus group discussions. The collected data were analyzed using thematic analysis. The research found that AI-supported learning tools were integrated extensively into MELR ecologies to help students learn vocabulary in multiple ways. Moreover, several developmental pathways in students’ SDL of vocabulary were revealed, illustrating how they managed tensions among learning objectives, self-regulation, contextual variations, and mediational tools. The findings suggest that AI may help reshape mediation, redistribute the division of labor, and catalyze transformations in self-directed vocabulary learning systems. The study also contributes to the literature by illustrating AI as an integrated, adaptable mediational tool within evolving MELR ecologies, offering insights into how AT can be applied to understand AI-assisted vocabulary learning.