Enterprise AI Analysis
Ai-Driven Cross-Media Narrative Design for Intangible Cultural Heritage: A Knowledge-Graph-Based Educational Framework
Author: YU YANG, Dalian Art College, Dalian, Liaoning, China
Published: April 1, 2026
Executive Impact: Key Performance Metrics
This research showcases significant advancements in leveraging AI for cultural education, yielding impressive results across several key dimensions.
82.5
System Usability Scale (SUS)
99%+
Content Generation Efficiency Increase
75%
Cultural Semantic Error Reduction
4.6
Cultural Understanding Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Addressing ICH Education Challenges with AI
The paper highlights critical challenges in Intangible Cultural Heritage (ICH) education, including fragmented learning contexts due to mobile internet, ineffective traditional models, and static, single-medium resources. It notes that current AI-generated content often lacks semantic accuracy and contextual understanding.
This research proposes an AI-powered cross-media narrative framework for ICH education, integrating educational needs analysis, cultural knowledge modeling, multimodal narrative generation, and learning assessment. Its aim is to provide a systematic approach for leveraging AI to create culturally accurate and engaging educational content.
Prior Art & Research Gaps
The study reviews existing advancements in Generative AI (AIGC) for cultural dissemination, noting its ability to produce multimodal educational content efficiently. It acknowledges the role of cross-media narrative design (Bruner, Transmedia Learning) in boosting engagement and comprehension.
It also discusses Knowledge Graphs (KGs) as structured semantic representation tools, crucial for ensuring cultural accuracy. However, the paper identifies key gaps: a lack of an integrated framework for AI, design, and education; insufficient cross-media generation with semantic control; and a deficit in system-level learning experience validation.
Knowledge-Graph-Based Framework
The proposed system integrates cultural semantic structures, AIGC multimodal generation, and educational narrative workflows. It starts with cultural semantic modeling, structuring ICH knowledge into "entity-attribute-relation" frameworks and extracting narrative data, mapping them to educational goal coding space.
A key innovation is the use of a culturally enhanced adjacency matrix within a graph convolutional network (GCN) to represent cultural entities and their relationships. This GCN model enables the system to generate cross-modal narrative content (text, images, voice) with semantic constraints, ensuring cultural consistency and educational accuracy.
Empirical Validation and Key Outcomes
The framework was tested with 60 participants. Key findings include a System Usability Scale (SUS) score of 82.5 ("Excellent"). Participants showed significant improvements in learning experiences, with a 4.6/5 score for cultural understanding and 4.5/5 for cross-media narrative experiences.
The system demonstrated high efficiency, achieving an over 99% increase in material generation efficiency for elements like pattern images and narrative texts. Critically, cultural semantic errors were reduced by 75-82%, confirming the knowledge graph's effectiveness in maintaining cultural accuracy.
Advancing ICH Preservation with AI
The study concludes that the AI-driven framework provides a new paradigm for ICH education by effectively integrating AI-generated content, cultural knowledge graphs, and cross-media narrative design. It successfully addresses issues of semantic fragmentation and inefficient resource production.
The framework enhances cultural comprehension, narrative immersion, content production efficiency, and cultural semantic consistency. Future work will focus on expanding multilingual knowledge graphs, multi-agent learning systems, and exploring intelligent narrative applications in international cultural communication.
99.93%
Reduction in Narrative Generation Time (45 min to 0.03 min)
Enterprise Process Flow: AI-Driven Narrative Generation
Cultural Semantic Modeling & Graph Construction
→
Multimodal Element Encoding & Feature Fusion
→
AIGC Narrative Generation (Text, Image, Voice)
→
Cross-Media Educational Narrative Design & Publication
| Metric | Traditional Manual Content | AI + KG Content | Improvement |
|---|---|---|---|
| Cultural Semantic Error Rate | 12.4% | 3.1% | ↓ 75% |
| Lack of Process Logic | 9.8% | 1.7% | ↓ 82% |
| Style Consistency Deviation | 15.3% | 4.2% | ↓ 72% |
Application Case: Revitalizing Intangible Cultural Heritage Education
This framework was applied to the challenge of embedding regional culture into interior space design and quantitatively assessing cultural identity, specifically referencing Dongguan Wanyi's cultural interior space. Traditional methods struggled with fragmented learning contexts and static resources, leading to ineffective transmission of ICH.
By leveraging AI-driven cross-media narrative design powered by a knowledge graph, the system demonstrated significant gains. It transformed experience-based cultural evaluation into a quantifiable and optimizable computational process, improving cultural understanding scores by an average of 4.6/5 and enhancing narrative immersion. This approach offers a scalable solution for preserving and disseminating ICH effectively in the digital age.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing AI solutions based on this research.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A phased approach for integrating AI-driven narrative design into your enterprise operations.
Phase 1: Knowledge Graph Foundation & Model Training
Establish the core cultural knowledge graph by collecting and structuring ICH data, semantic modeling, and constructing the graph structure. Begin initial training of AI models for fundamental understanding and generation capabilities, focusing on data preparation and foundational algorithms.
Phase 2: Multimodal AIGC Integration & Narrative Generation
Integrate Large Language Models, diffusion models for image/pattern generation, and speech synthesis. Apply knowledge graph constraints to ensure cultural accuracy. Develop and refine the multimodal content generation pipelines, producing initial cross-media narratives and educational content.
Phase 3: System Deployment & Educational Validation
Deploy the AI-driven cross-media narrative system for pilot educational applications. Conduct comprehensive user testing and learning experience assessments. Iterate based on feedback to optimize performance, usability, and cultural impact, ensuring effective ICH preservation and dissemination.
Ready to Transform Your Cultural Education?
Leverage the power of AI and Knowledge Graphs to create engaging, accurate, and scalable cross-media narratives for intangible cultural heritage. Schedule a personalized consultation to explore how this framework can be tailored to your specific needs.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat