<?xml version='1.0' encoding='utf-8'?>
<?xml-stylesheet type="text/xsl" href="/v2/static/oai2.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-10-14T01:48:33Z</responseDate>
  <request identifier="oai:figshare.com:article/34038186" metadataPrefix="oai_dc" verb="GetRecord">https://api.figshare.com/v2/oai</request>
  <GetRecord>
    <record>
      <header>
        <identifier>oai:figshare.com:article/34038186</identifier>
        <datestamp>2026-10-01T04:31:57Z</datestamp>
        <setSpec>category_129</setSpec>
        <setSpec>portal_316</setSpec>
        <setSpec>item_type_3</setSpec>
        <setSpec>month_year_10_2026</setSpec>
      </header>
      <metadata>
        <oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"  xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>Table 1_Reconfiguring graphic design education for generative AI in higher education: a human-agency and adaptive-curriculum framework.xlsx</dc:title>
          <dc:creator>Khaled Mostafa Ahmed Mohamed (25151817)</dc:creator>
          <dc:subject>Education</dc:subject>
          <dc:subject>curriculum transformation</dc:subject>
          <dc:subject>design education</dc:subject>
          <dc:subject>generative artificial intelligence</dc:subject>
          <dc:subject>higher education</dc:subject>
          <dc:subject>human agency</dc:subject>
          <dc:subject>mixed methods</dc:subject>
          <dc:description>Introduction&lt;p&gt;Generative AI creates a curriculum-transformation problem by redistributing activities through which disciplinary expertise is learned, practised, demonstrated, and assessed. This study examines that problem within graphic design education as a bounded empirical case.&lt;/p&gt;Methods&lt;p&gt;A survey of 270 participants was analysed using exploratory factor analysis (N = 265 complete cases), and semi-structured interviews with 12 design academics and practitioners were analysed using a frozen rule-based codebook across 650 meaning units. The quantitative and qualitative strands were analysed independently and integrated through a joint display.&lt;/p&gt;Results&lt;p&gt;The quantitative analysis yielded a broad, sample-specific Transformative–Normative response orientation and a narrower Curricular Diagnostic Critique cluster; neither is interpreted as a validated latent construct. The final qualitative dataset contained 504 normative/transformative units (77.5%), 35 diagnostic units (5.4%), and 111 units retained as X0 (17.1%). Both the N-family and D-family occurred in all 12 interviews.&lt;/p&gt;Discussion&lt;p&gt;The integrated interpretation informed the Bot-Haus Curriculum Architecture, which organises human creative agency, experiential human–AI practice, adaptive curriculum, critical and ethical governance, foundational design principles, and educational outcomes. The architecture is presented as an empirically informed, theory-guided design proposition developed from the graphic design case, not as a validated causal model. Transfer to other practice-based disciplines remains a proposition for future empirical testing.&lt;/p&gt;</dc:description>
          <dc:date>2026-10-01T04:31:57Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/feduc.2026.1972794.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Table_1_Reconfiguring_graphic_design_education_for_generative_AI_in_higher_education_a_human-agency_and_adaptive-curriculum_framework_xlsx/34038186</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
        </oai_dc:dc>
      </metadata>
    </record>
  </GetRecord>
</OAI-PMH>
