Teacher Development in the AI Era: Asian Perspectives

The growing use of Artificial Intelligence (AI) in education is reshaping the educational landscape and posing challenges to teachers’ capacity building in implementing AI-enabled classroom practices. Equipping teachers with the knowledge and skills to design AI-supported lessons has become an urgent priority in professional development, which can no longer rely on a one-size-fits-all approach. Learning AI-related knowledge is a complex and ongoing process in which teachers may engage in learning communities or networks that provide cross-boundary opportunities for collaboration and shared learning. Even so, controversial issues such as ethical considerations, the impact of AI on teacher creativity in teaching, and resource disparities have not yet been fully discussed or resolved. In response to this global issue, the adoption of AI in teacher education varies across countries, depending on the level of policy development and government support. Although support for AI education in many Asian countries has increased, limited research has examined its impacts, the challenges encountered, and the outcomes. Therefore, more attention is needed to understand how regional differences shape AI policy in teacher education and development.

AI adaptation in education has been viewed as an innovative practice in classroom teaching. It can enhance learning by making it more interactive and student-centered, supported by constructive feedback. However, its benefits require attention to two key issues: providing teacher development to equip teachers with both technical and pedagogical knowledge, and recognizing the context-sensitive nature of AI adaptation across different cultural settings. As a result, AI ethics has become important in ensuring an inclusive learning environment for students from diverse backgrounds. Recent studies on AI in education tend to focus more on teaching than on teacher development, representing a research gap that needs to be further addressed. As AI technology develops rapidly, teachers require continuous learning and regular updates. Such learning can also occur through collaboration across school communities, rather than relying solely on universities or government initiatives. Some Asian countries, such as China, South Korea, and Singapore, have taken leading roles in integrating AI into education for future workforce development in a technology-driven economy. Strong government initiatives have shaped AI education policies; however, discussions on teacher development and how different government inputs may create regional inequalities remain limited. Addressing this gap, this research topic adopts a critical perspective to examine teacher development in AI contexts, exploring both positive and negative experiences shaped by cultural and policy orientations in Asian countries.

This research topic welcomes both empirical studies and review articles, preferably adopting a critical perspective, to examine AI-enabled teacher education in Asian countries. Topics include:

1. Macro-level policy reviews of AI in teacher education and development in Asian countries;
2. Teachers’ learning and developmental needs when designing AI-enabled lessons;
3. Teacher perceptions of AI literacy in teaching, and their motivation and/or reservations about engaging in professional learning activities;
4. Knowledge gaps between teachers’ learning needs and AI literacy training provided through teacher development programs;
5. Challenges and cultural constraints affecting teachers’ use of AI in education;
6. Collaborative teacher learning practices and challenges in transforming AI into a learning partner in Asian schools;
7. Teacher capacity building for AI literacy and pedagogical practices in Asia, and emerging models of teacher development;
8. Teachers’ awareness of ethics, equity, and contextual adaptation of AI in Asian countries;
9. Partnership development for enhancing teachers’ AI literacy and the challenges encountered.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Teacher Development, Teacher Knowledge, AI in Education, Learning Community, Teacher Learning, Collaborative Learning, Boundary Crossing Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.