Enterprise AI Analysis
Research on AI-Enabled Course Teaching Based on Scientific Knowledge Mapping: Evolution Paths, Hotspots, and Frontiers
This study uses CiteSpace and scientific knowledge mapping to conduct a multidimensional analysis of 638 AI-enabled course teaching publications from the Web of Science Core Collection (2015-2024). Through quantitative textual analysis, this study outlines the overall framework of AI-enabled course teaching research, revealing that over the past decade, this field has undergone explosive growth. While China leads in total citations and publication quantity, the United States and other countries demonstrate higher citation-per-paper ratios. Core authors and institutions are concentrated in the U.S., and China, with educational technology journals as the main publication platforms, indicating field expansion but highlighting needs for international collaboration and quality improvement. Through bibliometric analysis and manual content summarization, this study systematically identifies: (1) Three evolutionary phases, technology framework construction (2015-2016), design application refinement (2017-2020), and efficacy-scenario expansion (2021-2024); (2) Four research clusters, smart technology-driven pedagogical innovation, immersive learning environment development, active learning and educational equity, and engineering education innovation and assessment; (3) Emerging frontiers, a shift from tool development to efficacy validation, with growing focus on deep learning, metaverse-based education, and academic early-warning systems. This research offers theoretical and practical implications for AI and course teaching integration. In addition, it identifies future needs to promote interdisciplinary collaboration and address ethical risks in educational large models.
Executive Impact: Key Metrics at a Glance
Key insights into the AI-enabled course teaching landscape, offering quantifiable impact areas for enterprise leaders.
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Total Publications Analyzed (2015-2024)
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Core Authors Identified
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Top Institution Citations (State University System of Florida)
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Total Citations (China)
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Top Citation-per-paper Ratio (USA)
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Research Growth (2020-2024 Peak)
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 field of AI-enabled course teaching has experienced explosive growth over the past decade. China leads in total publications and citations, reflecting a strong academic influence, though the U.S. demonstrates higher quality research with a superior citation-per-paper ratio. Core authors and institutions are concentrated in the U.S. and China, with educational technology journals being primary publication platforms.
Key findings highlight a progression from theoretical framework establishment to practical application and efficacy validation, emphasizing interdisciplinary collaboration and addressing ethical risks in educational large models.
The research has evolved through three distinct phases: (1) Technology Framework Construction (2015-2016), focusing on preliminary AI applications like adaptive learning platforms. (2) Design Application Refinement (2017-2020), shifting to specific methodologies, instructional design for AI, and personalized learning path design. (3) Efficacy-Scenario Expansion (2021-2024), concentrating on efficacy evaluation and broader application scenarios, especially in online education.
Current hotspots include smart technology-driven pedagogical innovation, immersive learning environment development (metaverse, virtual reality), active learning and educational equity (distance education, inclusive education), and engineering education innovation and assessment.
Emerging frontiers show a shift from tool development to efficacy validation, with growing interest in deep learning, metaverse-based education, and academic early-warning systems for predicting risks. Future needs include addressing ethical risks in LLMs and promoting interdisciplinary curriculum design.
638
Publications Analyzed (2015-2024)
Enterprise Process Flow
Technology Framework Construction (2015-2016)
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Design Application Refinement (2017-2020)
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Efficacy-Scenario Expansion (2021-2024)
| Country | Publications | Total Citations | Citation/Paper Ratio |
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| USA |
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| Spain |
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| South Korea |
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5.08
Strongest Keyword Burst (Deep Learning, 2022-2024)
AI in Engineering Education: A Case for Interdisciplinary Integration
The research highlights the deep integration of AI into engineering education systems, with 'engineering education' being an early burst keyword (2015-2019) and showing growing interdisciplinary connections. This demonstrates how AI moves beyond theoretical frameworks to practical application in specialized fields. It includes inquiry-based learning and STEM education quality assessment.
Outcome: Improved student engagement and performance through AI-driven personalized learning paths and feedback mechanisms. The synergy between AI and engineering education leads to more effective and equitable educational outcomes.
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Potential Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A strategic overview of the phased approach to integrating AI into your enterprise, ensuring a smooth and impactful transition.
Phase 1: Needs Assessment & AI Strategy Development
Identify core educational challenges and business objectives. Evaluate existing infrastructure and define a tailored AI strategy, including technology selection (e.g., adaptive learning platforms, intelligent tutoring systems) and ethical guidelines.
Phase 2: Pilot Program & Curriculum Integration
Implement AI-enabled teaching solutions in a pilot environment. Focus on instructional design, resource development, and personalized learning path integration for specific courses. Collect initial feedback and performance data.
Phase 3: Scalable Deployment & Continuous Optimization
Expand successful pilot programs across the institution. Implement advanced AI features like deep learning for content recommendation and academic early-warning systems. Establish continuous monitoring and optimization loops based on efficacy validation.
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