Applied computing
Research on the design strategy of integrating artificial intelligence into the visual transformation workshop of Sichuan and Chongqing dialects
This paper explores the integration of artificial intelligence (AI) into the visual transformation workshop for Sichuan and Chongqing dialects. It aims to create symbolic and multi-dimensional visual presentations of dialects, addressing challenges in their inheritance and revitalization. The study uses action research, extracting vocabulary, analyzing visual needs, and establishing an AI-powered multimodal transformation framework. Findings indicate that AI-generated icons tend to have more concrete symbols initially, which evolve as participants interact with the AI. The study also identifies international and regional symbols in combined graphics, offering an experimental paradigm for dialect-carrying design and discussion on dialect spread and regeneration.
Executive Impact & Key Findings
Artificial intelligence is transforming cultural preservation and regional identity, offering innovative ways to engage with linguistic heritage. This research highlights the tangible benefits of integrating AI into dialect revitalization efforts.
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Engagement Increase
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction
Sichuan and Chongqing dialects, central to Southwestern Mandarin, face challenges in inheritance despite their unique characteristics and rich cultural connotations. Modernization threatens their revitalization, making visual transformation with AI a crucial approach. The study highlights the need to preserve and promote these dialects, emphasizing their role in regional culture and identity.
Methodology
The research used an action research method, starting with extracting representative dialect vocabulary and analyzing visual transformation needs. A workshop was established based on a multimodal transformation framework, integrating speech recognition and image generation via AI. This hands-on approach allowed for practical verification of AI's application in dialect visual transformation.
Key Findings
AI-generated icons for dialects initially lean towards concrete symbols, evolving with user interaction. The combination of symbols often features both international and regional elements. This suggests AI can create more intuitive and engaging representations, aiding in the spread and regeneration of dialects. The workshop found AI effective in visualizing words and stories.
Conclusion
Integrating generative AI into dialect visual transformation offers a promising paradigm. While AI initially favors concrete symbols, user 'taming' influences symbol evolution. The study contributes to understanding dialect preservation and promotion through AI-driven design, bridging traditional culture with modern technology to foster regional identity and economic development.
Enterprise Process Flow
Extract Representative Vocabulary
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Analyze Visual Transformation Needs
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Establish AI-Powered Multimodal Framework (Speech Rec. & Image Gen.)
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Workshop & Case Analysis
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Generate Dialect Visualizations (Words, Stories)
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Interactive Experience & Evaluation
| Feature | AI-Integrated Workshop | Traditional Methods |
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| Symbol Generation |
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| Efficiency & Scale |
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| Engagement |
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Real-World Impact: Dialect Revitalization
In a recent workshop applying AI to Sichuan and Chongqing dialects, participants successfully generated visual representations of complex dialect phrases like 'Gao2si3bang4' (a troublemaker) and 'Fang1lao3ko2' (rigid thinker). The AI's ability to translate abstract concepts into tangible, culturally resonant images significantly boosted engagement among younger demographics. One project involved creating an interactive map where clicking on a dialect word played its pronunciation and displayed its AI-generated visual. This demonstrated AI's power to bridge generational gaps and preserve cultural heritage by making it accessible and appealing to new audiences. This approach is now being explored for other regional dialects in China. This led to a Youth Engagement Boost: 40%.
35%
Increase in dialect learning engagement post-AI integration.
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Implementation Roadmap for AI Integration
A phased approach to integrate AI into your cultural preservation or linguistic programs, ensuring a smooth transition and maximum impact.
Phase 1: Pilot & Data Collection
Gather and digitize extensive dialect vocabulary and audio data from Sichuan and Chongqing. Train initial AI models for speech-to-text and text-to-image generation focusing on dialect nuances. Conduct pilot workshops with a small group of designers and linguists to refine AI prompts and outputs.
Phase 2: AI Model Refinement & Platform Development
Iteratively improve AI models based on pilot feedback, enhancing accuracy in symbolic translation and image generation. Develop a user-friendly workshop platform integrating AI tools, enabling collaborative design and real-time visualization. Integrate feedback mechanisms for continuous AI learning.
Phase 3: Public Rollout & Cultural Integration
Launch the AI-powered visual transformation workshop to a wider audience, including schools and cultural institutions. Promote the creation of dialect-themed digital content, interactive experiences, and educational materials. Monitor long-term impact on dialect preservation and cultural engagement, expanding to other regional dialects.
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