<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>KMAN Publication Inc.</PublisherName>
      <JournalTitle>AI and Tech in Behavioral and Social Sciences</JournalTitle>
      <Issn></Issn>
      <Volume>3</Volume>
      <Issue>Serial Number 12</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>02</Month>
        <Day>11</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>AI Data Management Strategies in Tehran: A Gap Analysis Approach</ArticleTitle>
    <VernacularTitle>AI Data Management Strategies in Tehran: A Gap Analysis Approach</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>14</LastPage>
    <ELocationID EIdType="doi">10.61838/kman.aitech.3053</ELocationID>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <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>04</Month>
        <Day>19</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;Artificial intelligence (AI) is rapidly emerging as a primary driving force in the transformation of cities. This technology, by offering innovative solutions in various areas including transportation, energy, security, and urban services, has the potential to improve citizens’ quality of life and enhance urban sustainability. Tehran, with its growing population and increasing complexities, requires novel AI-based data management strategies. Effective data management is the key to improving urban services, reducing traffic congestion, increasing resource efficiency, and elevating citizens’ quality of life. Given AI’s high potential in processing vast volumes of urban data, this study aims to identify and examine the gaps through a gap analysis between the current state of data management in Tehran and successful strategies in leading cities, thereby exploring AI-driven innovative solutions. This exploratory research includes a comprehensive review of upstream urban development documents, master and detailed plans, municipal performance reports, and related legislative approvals on data management, along with a systematic search and analysis of scholarly articles published in reputable databases (such as Scopus and Web of Science) in the fields of smart city management, AI applications in cities, and successful case studies in other metropolitan areas. The study is grounded on the assumption that by identifying strengths and weaknesses in urban data management and utilizing successful global patterns, the potential of AI can be maximized to enhance the quality of life for Tehran’s citizens. The findings reveal the existence of significant challenges in urban management, strategic planning, and implementation when compared to cities such as Copenhagen, Stockholm, and Prague. While Tehran demonstrates a relatively acceptable performance in drafting urban strategies, a qualitative reassessment and fundamental revision in the depth and content of these strategies—tailored to local needs and aligned with international standards—appears to be essential.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Smart City</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Gap Analysis</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Data Management</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Data Roadmap</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journals.kmanpub.com/index.php/aitechbesosci/article/download/3053/7977</ArchiveCopySource>
  </Article>
</ArticleSet>
