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        <datestamp>2026-10-02T04:37:14Z</datestamp>
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          <dc:title>Supplementary file 1_Teachers’ use of generative artificial intelligence in K–12 education: a systematic review.docx</dc:title>
          <dc:creator>Oğuz Cincioğlu (25161414)</dc:creator>
          <dc:creator>Duygu Mutlu Bayraktar (25161417)</dc:creator>
          <dc:creator>Tuğba Altan (25161420)</dc:creator>
          <dc:subject>Education</dc:subject>
          <dc:subject>artificial intelligence in education</dc:subject>
          <dc:subject>generative artificial intelligence</dc:subject>
          <dc:subject>K-12 education</dc:subject>
          <dc:subject>teacher roles</dc:subject>
          <dc:subject>teachers</dc:subject>
          <dc:description>Background&lt;p&gt;The rapid advancement of generative artificial intelligence (GenAI) has created new opportunities and challenges for teachers, leading to a growing body of research on its educational applications. AI has become increasingly integrated into instructional processes, supporting teachers in tasks such as lesson planning, assessment, feedback generation, content creation, and classroom support. Despite the rapidly increasing number of studies on AI in education, the literature on teachers’ use of GenAI is distributed across different contexts, methodologies, and theoretical perspectives. Therefore, this systematic review aims to provide a comprehensive synthesis of research on teachers’ use of GenAI in Pre-12 and K-12 education.&lt;/p&gt;Methods&lt;p&gt;Following PRISMA 2020 guidelines, studies were screened and selected based upon the predefined inclusion and exclusion criteria, resulting in 79 empirical studies included in the final analysis. The analysis examined methodological trends, AI and GenAI technologies, functional and pedagogical roles, theoretical foundations, and key research focuses related to teachers’ use of GenAI.&lt;/p&gt;Results&lt;p&gt;The findings indicate a sharp increase in research output, particularly in recent years, with Asia as the most productive region. Qualitative and mixed-methods designs dominated the literature, while language education and STEM were the most frequently represented domains. Large language models emerged as the dominant GenAI type. Instructional design and co-planning represented some of the most prominent functional roles, highlighting a strong emphasis on teachers’ preparatory practices. The focal point of studies was on cognitive, affective, and pedagogical constructs, with technology acceptance frameworks such as Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) being most frequently employed.&lt;/p&gt;Conclusions&lt;p&gt;Overall, the findings reveal that GenAI is primarily positioned as a collaborative tool that supports teachers’ professional practice, particularly in instructional design and preparation, rather than replacing instructional roles. The review identifies key trends, gaps, and directions for future research on AI integration in education.&lt;/p&gt;</dc:description>
          <dc:date>2026-10-02T04:37:14Z</dc:date>
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          <dc:identifier>10.3389/feduc.2026.1935733.s001</dc:identifier>
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