Chais2025_Heb_and_Eng-web

32E Examining AI-Focused Professional Development Challenges findings reveal complex interrelationships between content-related barriers, critical content gaps, and operational challenges that extend beyond current frameworks and practices. Most notably, the study proposes three significant modifications to the Assessment area of the DigCompEdu AI Supplement: (1) expanding existing assessment-related challenges to better reflect the developmental stages of AI integration, from fundamental implementation to advanced applications, (2) adding a new challenge category under the Assessment area for " Ensuring Authenticity and Originality of Students' Work ", and (3) introducing a new challenge category under Professional Engagement for " Differentiated Professional Development " to address the unique complexities of delivering AI-focused TPD to diverse groups of educators. The study makes two key contributions. Theoretically, it expands the DigCompEdu AI Supplement by identifying gaps in its current conceptualization of AI-related challenges, particularly in assessment practices and TPD approaches. The proposed additions and modifications provide a more comprehensive and nuanced understanding of the challenges educators face when integrating AI into their practice. Practically, these findings highlight the need to begin with fundamental challenges such as managing AI-generated content, before advancing to more sophisticated applications. This understanding can help educational institutions design more effective TPD programs that align with teachers' actual needs and readiness levels. Limitations and Future Research This study's findings are limited by its focus on high school teachers in Israel who participated in an entry-level AI-focused TPD program. Reliance on self-report data may introduce biases like social desirability or recall inaccuracies. While demographic data including teaching experience and prior AI exposure was collected, detailed analysis of how these characteristics might influence teachers' experiences with AI-focused TPD is beyond the scope of this paper. Future research should broaden the scope to include teachers from diverse educational levels and cultural contexts. They may also employ mixed-methods instead of relying solely on qualitative research. In addition, evaluating different TPD models can help identify best practices for developing AI competencies. Finally, longitudinal studies could explore the long-term impacts of differentiated professional development on teachers' AI competencies and classroom practices. References Avidov-Ungar, O. (2024). The Personalized Continuing Professional Learning of Teachers: A Global Perspective (1st ed.) . Routledge. https://doi.org/10.4324/9781003424390 Bekiaridis, G., & Attwell, G. (2024). Supplement to the DigCompEDU Framework Introduction to AI in Education . Retrieved from https://aipioneers.org/supplement-to-the-digcompedu-framework/ Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology, 3(2), 77–101 . https://doi.org/10.1191/1478088706qp063oa Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments , 1–1 7. https://doi.org/10.1080/10494820.2023.2253861 Darling-Hammond, L., Hyler, M. E., Gardner, M. (2017). Effective Teacher Professional Development . Palo Alto, CA: Learning Policy Institute. https://doi.org/10.54300/122.311.