ARTIFICIAL INTELLIGENCE AND INTERNALIZATION IN EDUCATION: A NEW PARADIGM IN TEACHER EDUCATION PROGRAMS

The 21st-century educational landscape is being reshaped by Artificial Intelligence (AI), ushering in a new era of pedagogical transformation. Simultaneously, internalization in education—defined as the process of embedding international, intercultural, and global dimensions into the purpose, functions, and delivery of education—has gained traction as institutions aim to prepare learners for a globalized world. While these two trends independently contribute to educational reform, their intersection presents a promising yet underexplored avenue, especially in teacher education programs.

This research paper explores how AI and internalization can jointly redefine teacher education, enabling the development of globally competent educators who are technologically adept and culturally responsive. It outlines theoretical foundations, current global practices, and pedagogical innovations that combine AI-driven instruction with internalization principles.

AI tools such as intelligent tutoring systems, language translation software, personalized learning platforms, and predictive analytics are increasingly being used to tailor educational experiences. These tools, when incorporated into teacher education, not only enhance teaching skills but also expose prospective teachers to diverse cultural contexts. For example, AI-supported virtual exchange programs or multilingual AI chatbots allow pre-service teachers to engage with global peers, thereby internalizing international perspectives and practices.

This paper further analyzes how AI can aid in achieving internalization objectives by facilitating collaborative projects, intercultural simulations, and international benchmarking. It also examines the challenges related to ethics, data privacy, accessibility, and the potential depersonalization of teacher-student relationships. Addressing these concerns, the study recommends frameworks for integrating AI in a manner that enhances internalization rather than undermining it.

The research includes a review of recent literature from 2019 to 2025, highlighting key findings on the role of AI in teacher training and internalization strategies in education. Case studies from India, Finland, and Singapore are presented to illustrate practical implementation. These insights are used to suggest policy-level and institutional reforms that teacher education programs can adopt to make global learning more inclusive and technologically enabled.