Enterprise AI Analysis
Interdisciplinary Perspectives on Generative Artificial Intelligence Adoption in Higher Education: A Theoretical Framework Review
Our in-depth analysis of 'Interdisciplinary Perspectives on Generative Artificial Intelligence Adoption in Higher Education: A Theoretical Framework Review' reveals critical insights for enterprise AI strategy. This review synthesizes interdisciplinary perspectives to offer a comprehensive theoretical model for GenAI adoption in Higher Education.
Executive Impact Summary
Leveraging a systematic review methodology, this analysis underscores the complex interdisciplinary factors influencing Generative AI adoption in higher education. The findings provide a robust foundation for strategic planning and ethical implementation.
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Papers Reviewed
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Potentially Relevant Studies
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Initial Articles Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Interdisciplinary Research
Interdisciplinary Research in GenAI Adoption
This category synthesizes insights from psychology, computer science, and pedagogy to provide a holistic understanding of GenAI adoption in higher education. It emphasizes the need for comprehensive theoretical models that address cognitive, emotional, and ethical dimensions of technology acceptance, moving beyond traditional, narrow perspectives.
2.1 Technology Acceptance
Traditional models' limitations for AI
Enterprise Process Flow
Preliminary Screening
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Full-Text Review
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Final Selection (35 Articles)
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Synthesis & Model Proposal
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Ethical AI Frameworks in HE
The paper highlights the critical need for ethical and policy frameworks in GenAI adoption, drawing from initiatives by UNESCO, Nordic countries, and the European Union. These frameworks emphasize human-centered AI, focusing on principles like beneficence, non-maleficence, autonomy, and justice. The challenge lies in translating these broad principles into specific, actionable guidelines for educational settings, especially regarding data privacy, bias, and academic integrity. The proposed AI Ecological Education Policy Framework integrates these dimensions to guide ethical and practical AI use in higher education. Future research needs more empirical validation of these frameworks in real-world HE contexts.
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