Enterprise AI Analysis
A Visual Analysis of the Research Status and Prospects for the Integration and Development of Artificial Intelligence and Higher Education Based on CiteSpace
Authors: Yuhua Lin
Publication Date: June 06-08, 2025
Executive Impact: AI in Higher Education
This study conducted a visual analysis of 1,467 relevant literatures from the CNKI database (2015-2025) on the integration of Artificial Intelligence and Higher Education. Using CiteSpace, it identified research hotspots, author collaboration networks, and key development trends. The research revealed a shift from technological tool applications to educational ethics and talent cultivation, emphasizing a need for cross-disciplinary collaboration, technology-driven innovation, and enhanced digital literacy for a new intelligent education ecosystem.
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Research Papers Analyzed
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Years Covered
0
Avg Annual Growth Rate (AI/Edu papers)
0
Identified Hotspots
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research highlights a shift from focusing on technological applications to broader implications like educational ethics and talent cultivation. Early research centered on big data and information technology, evolving into discussions about 'smart education' and 'new engineering'.
Evolution of Research Focus
Technological Tools Application
→
Big Data & Information Technology
→
Educational Ethics & Talent Cultivation
→
Smart Education & New Engineering
→
Intelligent Education Ecosystem
38.6%
Average Annual Growth Rate of Publications post-2020
Analysis of author and institutional networks reveals a scattered collaboration landscape, particularly in western regions of China. Eastern regions show stronger cooperation driven by policy and funding, suggesting a need for cross-regional funding pools and replicating successful models.
| Aspect | Western Region | Eastern Region |
|---|---|---|
| Cooperation Density | 0.003 (Fragmented) | 0.012 (Significantly Higher) |
| Policy Support | Limited funds (18% of national funds) | Strong (e.g., Beijing AI Plan, Shanghai AI+Education pilot) |
| Growth Since 2019 | 12% or Stagnated | 58% |
0.0028
Author Collaboration Network Density (indicating low cooperation)
The study suggests building cross-disciplinary collaboration mechanisms, strengthening technology-driven innovation, and enhancing digital literacy. It also emphasizes the need for empirical research, ethical considerations, and dynamic curriculum adjustments to adapt to the new era of intelligent education.
Cultivating Digital Literacy in Higher Education
Description: To prepare students and faculty for the intelligent education ecosystem, enhancing digital literacy is critical. This involves not just tool proficiency but also ethical understanding and adaptability.
Challenge: Traditional curricula often lag behind rapid technological advancements, leaving a gap in digital competencies and ethical frameworks for AI use.
Solution: Integrate AI ethics into curriculum design, introduce human-machine collaboration courses, and establish dynamic discipline evaluation mechanisms. Implement continuous training for teachers on digital skills.
Outcome: Improved adaptability of graduates to AI-driven workplaces, a more ethically conscious teaching and learning environment, and a curriculum responsive to technological shifts.
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Potential Annual Savings
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Annual Hours Reclaimed
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Strategic Implementation Roadmap
A phased approach to integrating AI effectively into higher education, leveraging research insights for sustainable growth.
Phase 1: Foundation & Collaboration
Establish cross-disciplinary collaboration mechanisms and regional alliances. Secure funding for AI in education initiatives, focusing on balanced regional development. Conduct initial assessments of existing digital infrastructure and faculty AI readiness.
Phase 2: Technology & Curriculum Integration
Pilot AI-powered tools in specific courses. Develop new curricula integrating AI ethics, data science, and human-machine collaboration. Implement training programs for teachers to enhance their digital literacy and AI pedagogical skills.
Phase 3: Scaling & Ethical Governance
Expand successful pilot programs across departments. Develop university-wide policies for AI use, including data privacy and ethical guidelines. Establish a dynamic academic quality assurance mechanism to adapt specialties and resource allocation based on AI-driven market demands.
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