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            <name>Title</name>
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                <text>Faculty Publications</text>
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          <name>Creator</name>
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              <text>Shashi Rekha, B.V.; Kallarakal, Tomy K.; Thomas, Kennedy Andrew</text>
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              <text>A Smart Academic Ecosystem Framework for Enhancing Digital Skills and Startup Success Among Potential Women Entrepreneurs</text>
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              <text>01-01-2025</text>
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              <text>2025 World Conference on Cutting-Edge Science and Technology, WCCEST 2025;</text>
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              <text>&lt;a href="https://doi.org/10.1109/WCCEST66994.2025.11389884" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/WCCEST66994.2025.11389884&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105036495792?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105036495792?origin=resultslist&lt;/a&gt;</text>
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              <text>Shashi Rekha B.V., Christ Deemed to be University, Department of Commerce, Bangalore, India; Kallarakal T.K., Christ Deemed to be University, Department of Commerce, Bangalore, India; Thomas K.A., Christ Deemed to be University, Centre for Education, Bangalore, India</text>
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              <text>Lack of digital literacy is still a significant impediment for women entrepreneurs to participate and succeed in sectors that rely on technology infrastructure, as mentoring and incubation support is uneven, even for those who are skilled. Conventional academic programs tend to lack well-defined links between structured digital education and entrepreneurial careers, and, as a consequence, gaps between learning outcomes and startup success result. This paper introduces a Smart Academic Ecosystem (SAE) Framework, with tailored and interactive digital adaptation, AI-based mentorship, and dedicated startup incubation, pleading for a standard, one-size-fits-all system. The framework is implemented with a layered architecture comprising data ingestion (from learning management systems), feature stores (learner profiling), and algorithmic modules (knowledge tracing, contextual learning path recommendations, graph-based mentor matching, and venture readiness scoring). Fairness-enabling interventions and privacy-preserving analytics are built into the system to support fairness and trust. A pilot evaluation with early-stage women entrepreneurs identified substantial gains: digital skills mastery increased by more than 20%, startup initiation improved by 12 percentage points, and equity gaps in digital confidence and access were significantly narrowed. Findings emphasize that the SAE model leads not only to faster development of digital capability but also to higher chances of entrepreneurial success. This paper provides a replicable, standards-based, and computer-science-centred framework for academic institutions to encourage women entrepreneurs and innovation ecosystems.   2025 IEEE.</text>
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              <text>Adaptive Learning; AI-driven Mentorship; Computer Science Framework; Digital Skills; Entrepreneurship Education; Fairness-aware Algorithms; Knowledge Tracing; Smart Academic Ecosystem (SAE); Startup Incubation; Women Entrepreneurs</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>ISBN: 979-833156705-7;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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