<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Assessment and Practice in Educational Sciences</JournalTitle>
      <Issn>3092-717X</Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>09</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Designing a Conceptual Model of a Curriculum Based on Artificial Intelligence Literacy Education</ArticleTitle>
    <VernacularTitle>Designing a Conceptual Model of a Curriculum Based on Artificial Intelligence Literacy Education</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>16</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2025</Year>
        <Month>12</Month>
        <Day>25</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;The present study was conducted with the aim of designing a conceptual model of a curriculum based on artificial intelligence literacy education. This study adopted a qualitative approach and employed thematic analysis and the Glaserian approach as the research methodology. Since the study had an exploratory nature and was conducted with the purpose of expanding knowledge and increasing awareness in the field of artificial intelligence literacy education, it is categorized as an applied study. The statistical population included artificial intelligence specialists, education experts, curriculum planners, textbook authors, and secondary school teachers in West Azerbaijan Province. The sampling method was purposive and snowball sampling, which continued until theoretical saturation was achieved (15 interviews). The data analysis process was conducted in three stages: initial coding, axial coding, and selective coding. Finally, the collected and coded data were analyzed using MAXQDA software. Using thematic analysis, 85 initial open codes were extracted and categorized within the framework of conceptual components through axial and selective coding into six main dimensions, including cognitive competencies, skill-based competencies, attitudinal and ethical competencies, curriculum components, teaching–learning strategies and evaluation, and implementation requirements and contexts. At the end of the three-stage coding process, the final research model was developed through the integration of these dimensions, and the reliability of the research model was reported as 0.758.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Curriculum</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Artificial Intelligence Literacy</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Model Design</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journalapes.com/index.php/apes/article/download/251/218</ArchiveCopySource>
  </Article>
</ArticleSet>
