In this context, the reform of teacher education must begin with curriculum standards, learning outcomes, and pedagogical practice, alongside the capacity to select, verify, and utilise AI in a safe and responsible manner.
According to Nguyen Thu Thuy, Director of the International Cooperation Department under the Ministry of Education and Training, AI is evolving from a standalone support tool into a new foundational layer for teaching, assessment, administration, and professional development.
A professional AI competency framework for educators should be developed as an open reference framework—one that is human-centric, protects the rights of learners, and upholds the professional responsibilities of teachers.
This competency framework could focus on five key areas: foundational understanding and critical thinking regarding AI; ethics, legal aspects, data, and safety; AI-integrated instructional design and assessment; the use of AI for professional development and administrative tasks; and participation in the innovation, evaluation, and co-design of technology.
“In the immediate term, the competency framework should serve as a guiding and benchmarking document; following a pilot phase and impact assessment, core requirements can then be selected for integration into legally binding standards,” emphasised Thuy.
A critical requirement is to rethink how AI is incorporated into teacher education curricula. If AI is relegated to a single, isolated module, technological proficiency risks remaining detached from pedagogical expertise—even though AI is directly transforming how individual subjects are taught, how learning is assessed, and how educational activities are organised.
According to a representative from the School of Education at Vinh University, the legal framework for developing AI competencies among educators has taken shape quite rapidly; however, there remain gaps regarding the specific AI competency requirements for teachers across different educational levels and subjects.
For instance, the AI competencies required for a high school Literature teacher differ from those needed by an elementary school Mathematics teacher; therefore, training curricula must be appropriately differentiated.
The School of Education at Vinh University has proposed restructuring its training curriculum using a “core and periphery” model. The “core” consists of mandatory modules on AI in education for all teacher-training programs, covering AI fundamentals, data ethics and legal issues, and skills for utilising and evaluating AI tools. The “periphery” component is integrated into subject-specific pedagogical methodology courses.
For example, Mathematics education students learn to design assignments that remain effective even when traditional tasks can be solved by AI; Literature education students learn to evaluate written work in an era where AI can generate text; and Early Childhood and Elementary education students learn to organise age-appropriate learning experiences while ensuring the protection of children’s data.
This approach embeds AI directly into the process of developing professional teaching competencies, rather than treating it as a technological subject separate from pedagogical duties.
Furthermore, teacher-training institutions should establish guidelines for the use of AI in student learning, assessment, and examinations—fostering an environment that upholds academic integrity, data protection, and professional responsibility.
Tran Duc Manh, Head of the Academic Affairs Department at the University of Education, The University of Danang, noted that for the effective development of teachers’ professional AI competencies, there must be coordinated collaboration among state regulatory bodies, teacher-training institutions, and K-12 schools that serve as training sites.
Teacher training institutions should review their learning outcomes and incorporate AI-related competencies tailored to each specific discipline. This involves redesigning modules, learning materials, and practical activities to ensure that AI is integrated throughout the entire training process.
Assessment methods should also be modernised, shifting toward the use of e-portfolios, digital artifacts, project-based learning, and evidence demonstrating the ability to apply AI to professional tasks.
In practice, these requirements for reforming teacher training should be situated within a broader framework that encompasses curriculum innovation, pedagogical shifts, and institutional management.
According to Deputy Minister of Education and Training Le Quan, while digital transformation was previously the primary focus, AI has now emerged as a new catalyst driving profound changes in education and training.
The core challenge lies in restructuring human resources and enhancing their quality to boost labour productivity; in higher education, the objective is to improve the quality of training and restructure academic programs to meet development needs.
Deputy Minister Le Quan suggested that AI should be integrated into all aspects of education—from learning outcomes to curriculum content and pedagogical methods—through an interdisciplinary and transdisciplinary approach. AI is not limited to the realm of technology; it should also be incorporated into teacher training, educational psychology, communications, management, and various other fields.
Educational institutions need to proactively design new programs that equip learners with the ability to apply AI to solve practical educational challenges. Simultaneously, they should promote research and the testing of AI-based products, with the goal of identifying and scaling up effective models.
