VietTESOL International Convention 2026

In the context of digital transformation and the rapid development of artificial intelligence (AI), English self-directed learning activities in higher education are shifting from traditional learning models toward more personalized, flexible, and learner-autonomous approaches. Although studies on AI-assisted language learning have increasingly attracted scholarly attention, limited research has examined how AI is reframing English self-directed learning activities in highly disciplined and centralized educational environments such as Public Security Universities. From an educational perspective, this study investigates how AI is transforming the organization and development of English self-directed learning activities among cadets in Public Security Universities within the current context of digital transformation. The study employed a mixed-methods approach, surveying 1,000 cadets from first-year to fourth-year levels across four Public Security Universities, combined with semi-structured interviews with eight English lecturers, eight educational managers, and eight cadets. Quantitative data were analyzed using descriptive statistics, while qualitative data were examined through thematic analysis. The findings indicate that AI is shifting English self-directed learning from teacher- and textbook-dependent practices toward more personalized, adaptive, and self-regulated learning processes by providing immediate feedback, expanding opportunities for language practice, and diversifying learning resources. However, the study also identifies challenges related to technological dependence, reduced critical thinking, and academic integrity. Based on these findings, the study proposes educational implications for restructuring English self-directed learning activities in response to digital transformation within Public Security Universities.