Challenges and Opportunities for Digital Learning Resource Development: An Analysis of AI Application in Vietnamese General Education

The global integration of Artificial Intelligence (AI) into education is catalyzing a paradigm shift in the creation and utilization of Digital Learning Resources (DLRs). While promising transformative potential, the adoption of AI in developing nations faces unique systemic challenges. This study provides a comprehensive analysis of the current landscape, challenges, and opportunities of AI-driven DLR development within the general education system of Central Vietnam, a region characterized by specific socio-economic constraints. Employing a sequential explanatory mixed-methods design, we synthesized quantitative data from a survey of 454 teachers and administrators with in-depth qualitative data from focus groups and semi-structured interviews. Our findings reveal a significant dichotomy: while grassroots enthusiasm for AI is evident in the widespread adoption of accessible tools like ChatGPT for content creation, systemic barriers severely impede broader integration. Key impediments include inadequate technological infrastructure, a lack of cohesive national policy, and insufficient pedagogical training for educators. The study highlights a critical tension between national digital transformation ambitions and the on-the-ground realities faced by teachers. We argue that in this context, systemic factors such as infrastructure and policy act as primary determinants of technology adoption, potentially overriding individuallevel factors emphasized in traditional technology acceptance models (TAMs). The paper offers actionable recommendations for policymakers, school administrators, and professional development providers, aimed at fostering an equitable and sustainable ecosystem for AI in education. This research contributes a nuanced, context-specific perspective to the global discourse on educational technology, emphasizing the critical need for systemic support to unlock AI’s full potential in resource-constrained environments.