ندا کیانی نژاد

This study examines collegiality among Iranian EFL teachers by developing and validating the EFL Teacher Collegiality Scale (ETCS) through Mokken Scale Analysis (MSA). Collegiality, which we often understand as mutual respect, teamwork, and shared responsibility, is widely acknowledged as essential for promoting positive school environments and supporting teachers’ well-being. Specifically, collegiality supports teacher well-being, operationalized as job satisfaction, lower emotional exhaustion/burnout, and stronger professional efficacy. In Iran’s educational context, specific challenges make promoting teacher collaboration more difficult. To fill an important gap in the existing research, this study introduces a culturally adapted, reliable instrument designed to measure collegiality within this context. A cross-sectional survey using a 30-item ETCS was administered online to collect data from 170 teachers from various educational backgrounds. The ETCS is organized into three main areas: collaborative practice, professional support and mentorship, and shared professional development. The development process of the ETCS instrument involved expert review and back translation. Analysis showed that the scale has strong scalability and internal consistency, with high Mokken H coefficients across all subscales. The findings indicate that strong collegiality boosts teacher satisfaction, supports retention, and builds professional networks. Finally, this study provides practical strategies and a validated tool to improve EFL teacher collaboration and highlights online collegiality in remote or hybrid settings as a key topic for future research.

AI in Language Teaching, Learning, and Assessment (2024), edited by Fang Pan, offers a comprehensive and insightful exploration of how artificial intelligence (AI) is reshaping language education. The book is thoughtfully divided into three well-structured sections Preview, Perspectives, and Practice, each addressing essential dimensions of AI integration. In the Preview section, Pan examines AI's transformative role in enhancing educational delivery and personalizing learning experiences, paving the way for greater efficiency and accessibility. Subsequently, the Perspectives section collates diverse global viewpoints that critically address challenges such as algorithmic bias, data privacy, and equity concerns. Finally, the Practice section offers evidence-based case studies, showcasing tangible innovations such as adaptive tutoring systems and AI-powered assessment tools, which have demonstrated real-world impact. More importantly, Pan strikes an impressive balance between highlighting AI's vast potential and scrutinizing its ethical and practical complexities. By combining theoretical perspectives, empirical findings, and practical applications, this book stands as an essential resource for educators, researchers, and policymakers, aiming to navigate AI-driven developments in language education with both responsibility and clarity.