AI-Supported Language Learning for Non-Native English-Speaking Students: An Action Research
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Abstract
As linguistic diversity in higher education expands, non-native English-speaking students face persistent language barriers that hinder engagement, learning outcomes, and equity. This study investigates the impact of generative artificial intelligence (Gen-AI) in addressing these challenges through Walter, an AI-powered learning buddy designed to provide personalised linguistic and academic support. Framed by Sociocultural Theory, Walter functioned as a More Knowledgeable Other (MKO), scaffolding learners within their Zone of Proximal Development (ZPD) to enhance proficiency and foster academic participation. A mixed methods design integrated quantitative usage analytics and oral presentation performance (n = 30) with qualitative thematic analysis of student and instructor perspectives. Over a six-week intervention, analytics showed distinct patterns of in-campus and off-campus engagement, with usage peaks corresponding to high-stakes assessment preparation. Paired-sample t-tests revealed a statistically significant improvement in integrated oral presentation scores (p < .001), with a large effect size (d = 0.75) indicating substantial pedagogical impact. Qualitative findings identified three key themes: Walter as a psychologically safe space for rehearsal, its adaptive integration into diverse learning routines, and recognised limitations regarding conversational depth. While findings highlight Walter’s capacity to build skill and learner confidence, challenges regarding cultural sensitivity and contextual relevance remain. This study contributes empirical evidence for the role of Gen-AI in fostering inclusive higher education, recommending that institutions move beyond general-purpose AI toward discipline-specific, strategically integrated frameworks to ensure equitable and effective learning environments for all students.
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