Purpose: This study explores how lesson study (LS) can promote elementary Science, Technology, Engineering, and Mathematics (STEM) teachers’ professional development (TPD) in terms of new pedagogical practices, attitudes and beliefs in the maker education (ME) context.Design/methodology/approach: This is a case study of a LS conducted in China involving four primary school teachers, 20 grade-4 students, and one researcher who also acted as a facilitator. This study adopted an integrated model that combined the unique characteristics of Chinese LS (CLS) with the Dutch LS (LSNL) model.Findings: This study revealed that LS participation facilitates teachers’ integration of new ME pedagogical practices in their classrooms, while their attitudes and beliefs regarding teaching and learning are increasingly aligned with ME principles. However, challenges such as time constraints, lack of research skills, and insufficient learning resources have also been identified.Research limitations/implications: This was a small-scale study, which may limit the generalizability of the findings.Practical implications: This study expands the use of LS in the ME context by highlighting its effectiveness in enhancing teachers’ PD in terms of new pedagogical practices, attitudes, and beliefs. It also recommends incorporating diverse international LS models to address the limitations associated with localized models of TPD.Originality/value: The originality of this study lies in its adoption of an integrated LS model to enhance STEM teachers’ PD in an ME context. The findings of this study further strengthen evidence supporting the positive impact of LS on teachers’ PD.

Purpose: This study investigates how English as a Foreign Language (EFL) teachers from higher education develop and refine their curriculum design with Generative Artificial Intelligence (Gen-AI) collaboration during the Lesson Study (LS). Design/methodology/approach: Through a qualitative case study approach, we followed six English teachers in their collaborative work with a Gen-AI teaching assistant (Kimi) over a 6-month semester. Data were collected through the recordings of LS cycles, teacher interviews and reflections and documentation of teacher-AI interactions etc. Findings: The findings revealed three key aspects of Gen-AI integration in designing EFL curriculum: First, teachers progressively discovered Kimi’s capabilities in lesson planning, material development, and activity design, showing value in generating differentiated learning resources. Second, the teachers developed sophisticated collaboration patterns with the Gen-AI, demonstrating iterative refinement approaches and strategic integration of Gen-AI suggestions throughout the LS cycles. Third, teachers' critical reflections showed evolution in their evaluation and application of Gen-AI contributions, maintaining professional agency while leveraging Gen-AI capabilities effectively. Research limitations/implications: This study has several limitations that inform future research directions. Our investigation focused specifically on EFL higher education using a single Gen-AI tool (Kimi), which may limit the generalizability of the findings to other educational contexts and AI platforms. Practical implications: These findings suggest that Gen-AI integration through LS can enhance teachers' professional practice while promoting critical engagement with Gen-AI tools. The study provides insights into how Gen-AI can be meaningfully integrated into teacher professional development through collaborative LS approaches. Originality/value: The study demonstrates how the LS framework supports balanced AI integration while maintaining teacher agency. In addition, it reveals the process of AI capability discovery and strategic implementation in EFL teaching.

Maker Education (ME) focuses on fostering students’ Maker Mindsets (MM). However, prevailing ME practices often lack a structured approach, that impedes the effective development of students’ MM. To address this challenge, a Design Thinking-making pedagogy (DTMP) is used to design ME lessons through a three-month Lesson Study (LS) cycle. Employing a qualitative multiple case study methodology, we studied the development and transformation of MM in three elementary school students. Diverse data were collected, including student artifacts, video recordings of teacher discussions, and audio recordings of the interviews. The findings revealed common cognitive, self-efficacy, and collaboration improvements among the three students. Unique changes in individual cognitive skills, motivational patterns, and obstacles to collaboration were also observed. Furthermore, key factors influencing MM development were identified across individual, social, and pedagogical dimensions. Individual factors included prior knowledge, maker skills, and motivation, whereas social factors included teachers’ attitudes, recognition, feedback, and peer collaboration. Pedagogical practices, such as project difficulty, structure, and authenticity, play a crucial role in MM development. This study not only contributes valuable insights into MM development, but also underscores the pivotal role of tailored, personal real-life experiences in ME pedagogy. Furthermore, our study suggests that teachers can enhance ME pedagogy by intentionally designing it to facilitate MM in students, using the LS approach.

This study investigates how the integration of artificial intelligence reshapes the professional identity of university English teachers within higher education. Against the backdrop of global digital transformation, AI presents both disruptive potential and a significant ‘adaptation crises’ for educators. Using Bronfenbrenner’s Ecological Systems Theory as an analytical framework, this research systematically reviews the existing literature to address three objectives: (1) identify directions and typologies of teacher identity transformation; (2) analyse multilayered ecological influential factors; and (3) examine core challenges teachers face and their corresponding coping strategies. The findings indicate that professional identity is dynamically reconstructed across nested ecosystems, from micro-level classroom interactions to chronological level sociocultural contexts. This study advances an integrative perspective on the complex technology–teacher relationship, highlighting that successful identity transformation requires coordinated support across all ecological levels. Theoretical and practical implications are discussed to facilitate sustainable teacher development in the face of AI.