Introduction:
The rapid development of artificial intelligence (AI), particularly generative language models, presents both pedagogical opportunities and challenges for teacher education. This study introduces a model-driven and research-based framework for integrating AI tools in teacher education, aiming to enhance students’ metacognitive abilities, support lifelong learning, and promote equity in educational settings.

Two central research questions guide this study:
1) How can university lecturers, despite the rapid development of AI technologies, teach with the support of AI-powered tools in teacher education?
2) How can the integration of such tools into course design support teacher students’ metacognitive development, readiness for lifelong learning, and contribute to inclusive and motivating educational practices?

Method:
The study was conducted within the 15-credit course Educational Science Core II at Mid Sweden University. The course was redesigned to provide teacher students with a balanced combination of theoretical insight and practical experience in applying AI within educational contexts. Grounded in the frameworks of Technological Pedagogical Content Knowledge (TPACK), Substitution–Augmentation–Modification–Redefinition (SAMR), and the Traffic Light Model for responsible AI use, the intervention included two AI-focused modules.
Students engaged with various AI tools through assignments involving translation, lesson planning, learning environment design, and ethical reflection. The pedagogical framework fostered metacognition, critical thinking, and self-regulated learning, while also supporting the development of inclusive and equitable learning spaces. By integrating research-based modeling with hands-on exercises, the course demonstrated how AI can be sustainably embedded in teacher education practice and contribute to enhanced educational quality in an era of digital transformation.

Results:
Findings indicate that systematic use of AI-powered tools can effectively support teacher students’ metacognitive development and professional readiness. Students developed critical awareness of AI’s capabilities and limitations, practiced ethical decision-making, and demonstrated increased motivation for integrating AI in their future teaching practices. Features such as machine translation and multilingual support proved especially helpful in reducing linguistic and functional barriers, thereby contributing to more inclusive and equitable learning conditions.

Discussion:
The proposed framework, anchored in TPACK, SAMR, and the Traffic Light Model, offers a sustainable and pedagogically sound approach to AI integration in teacher education. By modeling both practical application and critical reflection in AI use, university lecturers can equip future teachers with the technological fluency and ethical discernment required for a digitally transforming educational landscape. The study concludes that well-structured, theory-informed AI integration supports quality education, metacognitive growth, and equity in diverse learning environments.

Keywords: Generative AI, ChatGPT, distance education, teacher education, metacognition, lifelong learning.