Chais2025_Heb_and_Eng-web

Maayan Shay Sayag, Ina Blau, Orit Avidov-Ungar 25E Keywords: Teacher Professional Development, Artificial Intelligence in Education, DigCompEdu AI Supplement Framework, AI Competency Development, Educational Technology. Literature review The emergence of Generative Artificial Intelligence (GenAI) has created unprecedented changes in educational practices and teacher responsibilities. This transformation requires teachers to develop new competencies to effectively utilize AI tools, while maintaining academic integrity and student well-being (Langran et al., 2024; Liu & Gu, 2024). As primary agents in developing students' AI literacy and preparing them for an AI-prevalent future, teachers face increasingly complex demands in their professional roles (NG et al., 2023). The successful integration of AI in education demands more than basic technical skills from teachers. Current research emphasizes that AI literacy encompasses essential knowledge, skills, and ethical principles needed to effectively use and critically evaluate GenAI tools in teaching practices (UNESCO, 2023). Teachers must understand AI capabilities and limitations, develop data literacy, and consider ethical implications in educational contexts (Langran et al., 2024). However, developing these comprehensive competencies presents significant challenges that require targeted professional development solutions (Bekiaridis & Attwell, 2024). The AI supplement to the DigCompEdu framework, developed as part of the AI Pioneers project, builds upon the European Digital Competence Framework for Educators by integrating AI-specific competencies across its six key areas: Professional Engagement, Digital Resources, Teaching and Learning, Assessment, Empowering Learners, and Facilitating Learners' Digital Competence (Bekiaridis & Attwell, 2024).This framework highlights the interconnected nature of technical skills, pedagogical implementation, and ethical considerations in AI integration. Within each area, the model identifies various challenges, from data privacy concerns and technological barriers to algorithmic bias issues, with teacher preparedness emerging as a critical challenge across all categories. Teachers’ Professional development (TPD) plays a vital role in supporting teachers' AI competency development. Effective TPD programs incorporate key characteristics such as content focus, active learning, coaching and expert feedback, alignment with school goals, sustained duration, and joint participation (Darling-Hammond et al., 2017). Recent approaches emphasize personalization, suggesting that program effectiveness improves through dedicated practice time, ongoing post-training support, and strong institutional backing (Avidov-Ungar, 2024). The rapid advancement of GenAI technologies further emphasizes the importance of thoughtful TPD design and implementation, requiring programs to address both technical proficiency and pedagogical integration while considering teachers' attitudes and organizational support (Skantz-Åberg et al., 2022). Early studies of AI-focused TPD programs reveal significant implementation challenges. While these programs successfully enhance teachers' theoretical understanding of AI concepts and ethical awareness, they often struggle to support practical classroom implementation (Kong & Yang, 2024). Teachers frequently report difficulties translating theoretical knowledge into teaching practices, particularly when designing innovative AI-integrated lessons (Ding et al., 2024; Meli et al., 2024). A critical gap exists in understanding how TPD programs affect the development of teachers' AI competencies in educational contexts. Current research primarily focuses on short-term outcomes or specific technical AI-related skills (Chiu et al., 2023; Ding et al., 2024; Kong & Yang, 2024), leaving broader questions about AI competency development unexplored. Notably