Chais2025_Heb_and_Eng-web

28E Examining AI-Focused Professional Development Challenges Sub-category 1.2: Perceived Lack of Value and Relevance Barriers (n=64) Professional Engagement: • Resistance to Change and Technological Integration Teaching & Learning: • Teacher Preparedness and Training • Integrating AI with Existing Practices "I have plenty of materials. Editing an existing exam takes less time than creating something new with GPT and checking all its nuances." (T7) "For physics in grades 11-12, you need deep understanding of the material and knowledge of students' difficulties. I don't need AI for that... I won't use it extensively in physics." (T12) "I have excellent presentations that are didactically sound and appropriate for what I teach. I can't say I'll use AI for that." (T14) "The image generation tools are good for backgrounds and aesthetics, but not for actual teaching material in physics." (T18) The findings highlight two key barriers that reflect core challenges identified in the DigCompEdu AI Supplement. First, Tools Limitations and Usage Barriers manifest in Hebrew language processing and generating accurate visual content across all subject areas, aligning with the framework's "Technical Limitations and Reliability" challenge. Additionally, the "Content Quality and Relevance" challenge emerged as a significant concern, with AI-generated materials often failing to meet curricular requirements across different subjects—from sciences to humanities. These challenges created significant time burdens through an iterative process of correction and adaptation, reflecting the "Teacher Preparedness and Training" challenge, and highlighted the complexity of preparing educators to effectively use AI tools in teaching practices (Kong & Yang, 2024). The second barrier, perceived lack of value and relevance , reflected challenges across multiple DigCompEdu AI Supplement competency areas - "Resistance to Change and Technological Integration" under Professional Engagement, and both "Integrating AI with Existing Practices" and "Teacher Preparedness and Training" under Teaching and Learning. Experienced teachers particularly questioned AI's pedagogical value, preferring existing materials over investing time in mastering AI tools and expressing skepticism about AI's ability to enhance teaching practices. This resistance was especially pronounced in subject-specific contexts, where the perceived investment in developing AI proficiency outweighed uncertain benefits. These findings align with research on teachers' challenges in translating AI knowledge into meaningful applications (Ding et al., 2024; Kong & Yang, 2024) and concerns about AI integration among experienced educators (Langran et al., 2024). Theme 2: Critical Content Gaps in AI-Focused TPD Analysis of teachers' experiences revealed three significant gaps in the TPD program content: Need for Training in AI-Adapted Assessment Practices , ethical considerations , and Lack of Pedagogical Guidelines for Classroom Implementation . These missing components significantly impacted teachers' ability to develop comprehensive AI competencies and effectively implement AI tools in their teaching practice. These challenges align with multiple categories in the DigCompEdu AI Supplement, particularly within Professional Engagement, Assessment and