*Result*: AI-Driven Culturally Aware Interactive Visualization: A Design Methodology for Cross-Cultural User Experience.
Title:
AI-Driven Culturally Aware Interactive Visualization: A Design Methodology for Cross-Cultural User Experience.
Authors:
Liang X; School of Art and Design, Guilin University of Tourism, Guilin, China.
Source:
Annals of the New York Academy of Sciences [Ann N Y Acad Sci] 2026 Feb; Vol. 1556 (1), pp. e70198.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: New York Academy of Sciences Country of Publication: United States NLM ID: 7506858 Publication Model: Print Cited Medium: Internet ISSN: 1749-6632 (Electronic) Linking ISSN: 00778923 NLM ISO Abbreviation: Ann N Y Acad Sci Subsets: MEDLINE
Imprint Name(s):
Publication: 2006- : New York, NY : Malden, MA : New York Academy of Sciences ; Blackwell
Original Publication: New York, The Academy.
Original Publication: New York, The Academy.
MeSH Terms:
References:
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Y. Ye, Z. Chen, and A. Wu, et al., “Generative AI for Visualization: State of the Art and Future Directions,” Computers & Graphics 119 (2024): 136–149, https://doi.org/10.1016/j.cag.2024.03.003.
A. Marcus and E. W. Gould, “Crosscurrents: Cultural Dimensions and Global Web User‐Interface Design,” Interactions 7, no. 4 (2000): 32–46, https://doi.org/10.1145/345190.345238.
S. Liu, W. Yang, and J. Wang, Visualization for Artificial Intelligence (Springer Nature, 2024), https://doi.org/10.1007/978‐3‐031‐75340‐4.
Y. Ye, J. Hao, Y. Hou, et al., “Generative AI for Visualization: State‐of‐the‐Art and Future Directions,” Visual Informatics 8, no. 2 (2024): 43–66, https://doi.org/10.1016/j.visinf.2024.04.003.
Q. Wang, Z. Chen, Y. Wang, and H. Qu, “VIS+AI: Integrating Visualization With Artificial Intelligence for Efficient Data Analysis,” Frontiers of Computer Science 17, no. 6 (2023): 176701, https://doi.org/10.1007/s11704‐023‐2691‐y.
C. Chen, J. Yuan, Y. Lu, et al., “OoDAnalyzer: Interactive Analysis of Out‐of‐Distribution Samples,” IEEE Transactions on Visualization and Computer Graphics 27, no. 7 (2020): 2335–2349.
R. C. Basole, T. Major, and F. Ferrise, “Generative AI for Visualization: Opportunities and Challenges,” IEEE Computer Graphics and Applications 44, no. 2 (2024): 55–64, https://doi.org/10.1109/MCG.2024.3368945.
B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” in The Craft of Information Visualization, ed. B. B. Bederson and B. Shneiderman (Elsevier, 2003), 364–371.
C. Ware, Information Visualization: Perception for Design, 4th ed. (Morgan Kaufmann Publishers, 2019).
J. S. Yi, Y. A. Kang, J. Stasko, and J. Jacko, “Toward a Deeper Understanding of the Role of Interaction in Information Visualization,” IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (2007): 1224–1231.
Z. Liu and J. Heer, “The Effects of Interactive Latency on Exploratory Visual Analysis,” IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2122–2131.
N. Elmqvist and J. S. Yi, “Patterns for Visualization Evaluation,” Information Visualization 14, no. 3 (2015): 250–269.
J. C. Roberts, “State of the Art: Coordinated & Multiple Views in Exploratory Visualization,” in Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (IEEE, 2007), 61–71.
K. Reinecke and A. Bernstein, “Knowing What a User Likes: A Design Science Approach to Interfaces That Automatically Adapt to Culture,” MIS Quarterly 37, no. 2 (2013): 427–453.
D. Cyr, M. Head, and H. Larios, “Colour Appeal in Website Design Within and Across Cultures: A Multi‐Method Evaluation,” International Journal of Human‐Computer Studies 68, no. 1–2 (2010): 1–21.
P. Bourges‐Waldegg and S. A. Scrivener, “Meaning, the Central Issue in Cross‐Cultural HCI Design,” Interacting with Computers 9, no. 3 (1998): 287–309.
Y. Y. Choong and G. Salvendy, “Design of Icons for Use by Chinese in Mainland China,” Interacting with Computers 9, no. 4 (1998): 417–430.
A. S. Smith and E. Chang, “Cross‐Cultural Interface Design Strategy,” in Proceedings of the International Conference on Information Technology (IEEE, 2006), 125–135.
N. Iivari, “Discourses on ‘Culture’ and ‘Usability Work’ in Software Product Development,” ACM Transactions on Computer‐Human Interaction 13, no. 1 (2006): 1–36.
E. Hoque and G. Carenini, “MultiConVis: A Visual Text Analytics System for Exploring a Collection of Online Conversations,” in Proceedings of the 21st International Conference on Intelligent User Interfaces (ACM, 2016), 96–107.
C. Shi, S. Liu, H. Tong, H. Qu, P. S. Yu, and J. Wu, “EgoSlider: Visual Analysis of Egocentric Network Evolution,” IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 260–269.
Z. Yang, M. Sedlmair, and T. Munzner, “Comparative Evaluation of Bipartite, Node‐Link, and Matrix‐Based Network Representations,” ACM Transactions on Computer‐Human Interaction 24, no. 2 (2017): 1–40.
J. Zhao, M. Glueck, F. Chevalier, Y. Wu, and A. Khan, “Egocentric Analysis of Dynamic Networks With EgoLines,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (ACM, 2016), 5003–5014.
M. Ali, N. Riaz, I. Ashraf, A. Sohail, and M. A. Khan, “Accurate Multi‐Criteria Decision Making Methodology for Recommending Machine Learning Algorithm,” Expert Systems with Applications 71 (2017): 257–278, https://doi.org/10.1016/j.eswa.2016.11.034.
J. Zhang, Y. Wang, P. Molino, L. Li, and D. S. Ebert, “Manifold: A Model‐Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,” IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (2019): 364–373.
N. Houssami , G. Kirkpatrick‐Jones , N. Noguchi , and C. I. Lee, “Artificial Intelligence (AI) for the Early Detection of Breast Cancer: A Scoping Review to Assess AI's Potential in Breast Cancer Screening Programs,” Expert Review of Medical Devices 16, no. 5 (2019): 351–362.
A. Kamilaris and F. X. Prenafeta‐Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture 147 (2018): 70–90.
Y.‐H. Chen , E. J.‐L. Lu, and S.‐C. Lin, “Ontology‐Based Dynamic Semantic Annotation for Social Image Retrieval,” in 2020 21st IEEE International Conference on Mobile Data Management (MDM) (IEEE, 2020), 337–341., https://doi.org/10.1109/MDM48529.2020.00071.
International Council of Museums, Digital Cultural Heritage Collections Database (2023). Retrieved from, https://icom.museum/en/resources/standards‐guidelines/digital‐collections/.
A. Mosca and G. D. Magoulas, “Customised Ensemble Methodologies for Deep Learning: Boosted Residual Networks and Related Approaches,” Neural Computing and Applications 31, no. 6 (2019): 1713–1731.
D. Bahdanau , K. Cho , and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate (2014). arXiv:1409.0473.
R. H. Kazi , F. Chevalier , T. Grossman , and G. Fitzmaurice, “Kitty: Sketching Dynamic and Interactive Illustrations,” in Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology (ACM, 2014), 395–405.
S. Liu, W. Cui, Y. Wu, and M. Liu, “A Survey on Information Visualization: Recent Advances and Challenges,” Visual Computer 30, no. 12 (2014): 1373–1393.
Y. Duan, F. Andrieux, and F. Kuester, “Reinforcement Learning for Adaptive Dialogue Systems,” in Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue (Association for Computational Linguistics, 2017), 91–96.
Y. Wang, Q. Chen, M. Hong, X. Wang, and D. Tao, “Attentive Normalization for Conditional Image Generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (Seattle, WA, USA, 2020), 5094–5103, https://doi.org/10.1109/CVPR42600.2020.00514.
F. Beck and D. Weiskopf, “A Visual Analytics Perspective on Dynamic Networks,” in Proceedings of the Eurographics Conference on Visualization (Eurographics Association, 2017), 375–379.
X. Pu , M. Kay , S. Wang , E. M. Peck, and L. Harrison, “Trajectory Bundling for Animated Transitions,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (ACM, 2018), 1–13.
C. Turkay , F. Jeanquartier , A. Holzinger , and H. Hauser, “On Computationally‐Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics,” in Interactive Knowledge Discovery and Data Mining in Biomedical Informatics (Springer, 2014), 117–140.
M. Sedlmair , M. Meyer , and T. Munzner, “Design Study Methodology: Reflections From the Trenches and the Stacks,” IEEE Transactions on Visualization and Computer Graphics 18, no. 12 (2012): 2431–2440.
N. Ranjbar Kermany , J. Yang, and W. Zhao, “Incorporating User Rating Credibility in Recommender Systems,” Future Generation Computer Systems 142 (2023): 293–308, https://doi.org/10.1016/j.future.2023.02.008.
J. Zhang and Y. Wang, “Digital Media Recommendation System Design Based on User Behavior Analysis and Emotional Feature Extraction,” PLoS ONE 19, no. 12 (2024): e0322768, https://doi.org/10.1371/journal.pone.0322768.
D. Helic , U. Gadiraju, and M. Tkalcic, “Editorial: User Modeling and Recommendations,” Frontiers in Big Data 5 (2022): 902764, https://doi.org/10.3389/fdata.2022.902764.
D. Sacha , A. Stoffel , F. Stoffel , B. C. Kwon , G. Ellis, and D. A. Keim, “Knowledge Generation Model for Visual Analytics,” IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 1604–1613.
S. Sarwar, A. Dutta, C. Li, et al., “Training Artificial Neural Network With a Cultural Algorithm,” Neural Processing Letters 53, no. 6 (2021): 4637–4674, https://doi.org/10.1007/s11063‐024‐11636‐7.
Z. Chen , Y. Wang, Q. Wang, S. Liu, and H. Qu , “Visual Abstraction and Exploration of Multi‐Class Scatterplots,” IEEE Transactions on Visualization and Computer Graphics 26, no. 6 (2020): 2291–2304.
A. Endert, W. Ribarsky, C. Turkay, et al., “The State of the Art in Integrating Machine Learning Into Visual Analytics,” Computer Graphics Forum 36, no. 8 (2017): 458–486.
M. Liu , J. Shi , Z. Li , C. Li , J. Zhu , and S. Liu, “Towards Better Analysis of Deep Convolutional Neural Networks,” IEEE Transactions on Visualization and Computer Graphics 23, no. 1 (2017): 91–100.
K. Kurzhals and D. Weiskopf, “Space‐Time Visual Analytics of Eye‐Tracking Data for Dynamic Stimuli,” IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (2013): 2129–2138.
T. Blascheck, K. Kurzhals, M. Raschke, M. Burch, D. Weiskopf, and T. Ertl, “Visualization of Eye Tracking Data: A Taxonomy and Survey,” Computer Graphics Forum 36, no. 8 (2017): 260–284.
Y. Zou, S. Pintong, T. Shen, and D. B. Luh, “Cross‐Cultural Design in Costume: Case Study on Totemic Symbols of China and Thailand,” Humanities and Social Sciences Communications 11, no. 1 (2024): 1573, https://doi.org/10.1057/s41599‐024‐03707‐w.
N. Bikakis, Big Data Visualization Tools (2018). arXiv:1801.08336. Retrieved from, https://arxiv.org/abs/1801.08336.
S. K. Card, J. D. Mackinlay, and B. Shneiderman, Readings in Information Visualization: Using Vision to Think (Morgan Kaufmann Publishers, 1999).
Y. Ye, Z. Chen, and A. Wu, et al., “Generative AI for Visualization: State of the Art and Future Directions,” Computers & Graphics 119 (2024): 136–149, https://doi.org/10.1016/j.cag.2024.03.003.
A. Marcus and E. W. Gould, “Crosscurrents: Cultural Dimensions and Global Web User‐Interface Design,” Interactions 7, no. 4 (2000): 32–46, https://doi.org/10.1145/345190.345238.
S. Liu, W. Yang, and J. Wang, Visualization for Artificial Intelligence (Springer Nature, 2024), https://doi.org/10.1007/978‐3‐031‐75340‐4.
Y. Ye, J. Hao, Y. Hou, et al., “Generative AI for Visualization: State‐of‐the‐Art and Future Directions,” Visual Informatics 8, no. 2 (2024): 43–66, https://doi.org/10.1016/j.visinf.2024.04.003.
Q. Wang, Z. Chen, Y. Wang, and H. Qu, “VIS+AI: Integrating Visualization With Artificial Intelligence for Efficient Data Analysis,” Frontiers of Computer Science 17, no. 6 (2023): 176701, https://doi.org/10.1007/s11704‐023‐2691‐y.
C. Chen, J. Yuan, Y. Lu, et al., “OoDAnalyzer: Interactive Analysis of Out‐of‐Distribution Samples,” IEEE Transactions on Visualization and Computer Graphics 27, no. 7 (2020): 2335–2349.
R. C. Basole, T. Major, and F. Ferrise, “Generative AI for Visualization: Opportunities and Challenges,” IEEE Computer Graphics and Applications 44, no. 2 (2024): 55–64, https://doi.org/10.1109/MCG.2024.3368945.
B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” in The Craft of Information Visualization, ed. B. B. Bederson and B. Shneiderman (Elsevier, 2003), 364–371.
C. Ware, Information Visualization: Perception for Design, 4th ed. (Morgan Kaufmann Publishers, 2019).
J. S. Yi, Y. A. Kang, J. Stasko, and J. Jacko, “Toward a Deeper Understanding of the Role of Interaction in Information Visualization,” IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (2007): 1224–1231.
Z. Liu and J. Heer, “The Effects of Interactive Latency on Exploratory Visual Analysis,” IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2122–2131.
N. Elmqvist and J. S. Yi, “Patterns for Visualization Evaluation,” Information Visualization 14, no. 3 (2015): 250–269.
J. C. Roberts, “State of the Art: Coordinated & Multiple Views in Exploratory Visualization,” in Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (IEEE, 2007), 61–71.
K. Reinecke and A. Bernstein, “Knowing What a User Likes: A Design Science Approach to Interfaces That Automatically Adapt to Culture,” MIS Quarterly 37, no. 2 (2013): 427–453.
D. Cyr, M. Head, and H. Larios, “Colour Appeal in Website Design Within and Across Cultures: A Multi‐Method Evaluation,” International Journal of Human‐Computer Studies 68, no. 1–2 (2010): 1–21.
P. Bourges‐Waldegg and S. A. Scrivener, “Meaning, the Central Issue in Cross‐Cultural HCI Design,” Interacting with Computers 9, no. 3 (1998): 287–309.
Y. Y. Choong and G. Salvendy, “Design of Icons for Use by Chinese in Mainland China,” Interacting with Computers 9, no. 4 (1998): 417–430.
A. S. Smith and E. Chang, “Cross‐Cultural Interface Design Strategy,” in Proceedings of the International Conference on Information Technology (IEEE, 2006), 125–135.
N. Iivari, “Discourses on ‘Culture’ and ‘Usability Work’ in Software Product Development,” ACM Transactions on Computer‐Human Interaction 13, no. 1 (2006): 1–36.
E. Hoque and G. Carenini, “MultiConVis: A Visual Text Analytics System for Exploring a Collection of Online Conversations,” in Proceedings of the 21st International Conference on Intelligent User Interfaces (ACM, 2016), 96–107.
C. Shi, S. Liu, H. Tong, H. Qu, P. S. Yu, and J. Wu, “EgoSlider: Visual Analysis of Egocentric Network Evolution,” IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 260–269.
Z. Yang, M. Sedlmair, and T. Munzner, “Comparative Evaluation of Bipartite, Node‐Link, and Matrix‐Based Network Representations,” ACM Transactions on Computer‐Human Interaction 24, no. 2 (2017): 1–40.
J. Zhao, M. Glueck, F. Chevalier, Y. Wu, and A. Khan, “Egocentric Analysis of Dynamic Networks With EgoLines,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (ACM, 2016), 5003–5014.
M. Ali, N. Riaz, I. Ashraf, A. Sohail, and M. A. Khan, “Accurate Multi‐Criteria Decision Making Methodology for Recommending Machine Learning Algorithm,” Expert Systems with Applications 71 (2017): 257–278, https://doi.org/10.1016/j.eswa.2016.11.034.
J. Zhang, Y. Wang, P. Molino, L. Li, and D. S. Ebert, “Manifold: A Model‐Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,” IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (2019): 364–373.
N. Houssami , G. Kirkpatrick‐Jones , N. Noguchi , and C. I. Lee, “Artificial Intelligence (AI) for the Early Detection of Breast Cancer: A Scoping Review to Assess AI's Potential in Breast Cancer Screening Programs,” Expert Review of Medical Devices 16, no. 5 (2019): 351–362.
A. Kamilaris and F. X. Prenafeta‐Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture 147 (2018): 70–90.
Y.‐H. Chen , E. J.‐L. Lu, and S.‐C. Lin, “Ontology‐Based Dynamic Semantic Annotation for Social Image Retrieval,” in 2020 21st IEEE International Conference on Mobile Data Management (MDM) (IEEE, 2020), 337–341., https://doi.org/10.1109/MDM48529.2020.00071.
International Council of Museums, Digital Cultural Heritage Collections Database (2023). Retrieved from, https://icom.museum/en/resources/standards‐guidelines/digital‐collections/.
A. Mosca and G. D. Magoulas, “Customised Ensemble Methodologies for Deep Learning: Boosted Residual Networks and Related Approaches,” Neural Computing and Applications 31, no. 6 (2019): 1713–1731.
D. Bahdanau , K. Cho , and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate (2014). arXiv:1409.0473.
R. H. Kazi , F. Chevalier , T. Grossman , and G. Fitzmaurice, “Kitty: Sketching Dynamic and Interactive Illustrations,” in Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology (ACM, 2014), 395–405.
S. Liu, W. Cui, Y. Wu, and M. Liu, “A Survey on Information Visualization: Recent Advances and Challenges,” Visual Computer 30, no. 12 (2014): 1373–1393.
Y. Duan, F. Andrieux, and F. Kuester, “Reinforcement Learning for Adaptive Dialogue Systems,” in Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue (Association for Computational Linguistics, 2017), 91–96.
Y. Wang, Q. Chen, M. Hong, X. Wang, and D. Tao, “Attentive Normalization for Conditional Image Generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (Seattle, WA, USA, 2020), 5094–5103, https://doi.org/10.1109/CVPR42600.2020.00514.
F. Beck and D. Weiskopf, “A Visual Analytics Perspective on Dynamic Networks,” in Proceedings of the Eurographics Conference on Visualization (Eurographics Association, 2017), 375–379.
X. Pu , M. Kay , S. Wang , E. M. Peck, and L. Harrison, “Trajectory Bundling for Animated Transitions,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (ACM, 2018), 1–13.
C. Turkay , F. Jeanquartier , A. Holzinger , and H. Hauser, “On Computationally‐Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics,” in Interactive Knowledge Discovery and Data Mining in Biomedical Informatics (Springer, 2014), 117–140.
M. Sedlmair , M. Meyer , and T. Munzner, “Design Study Methodology: Reflections From the Trenches and the Stacks,” IEEE Transactions on Visualization and Computer Graphics 18, no. 12 (2012): 2431–2440.
N. Ranjbar Kermany , J. Yang, and W. Zhao, “Incorporating User Rating Credibility in Recommender Systems,” Future Generation Computer Systems 142 (2023): 293–308, https://doi.org/10.1016/j.future.2023.02.008.
J. Zhang and Y. Wang, “Digital Media Recommendation System Design Based on User Behavior Analysis and Emotional Feature Extraction,” PLoS ONE 19, no. 12 (2024): e0322768, https://doi.org/10.1371/journal.pone.0322768.
D. Helic , U. Gadiraju, and M. Tkalcic, “Editorial: User Modeling and Recommendations,” Frontiers in Big Data 5 (2022): 902764, https://doi.org/10.3389/fdata.2022.902764.
D. Sacha , A. Stoffel , F. Stoffel , B. C. Kwon , G. Ellis, and D. A. Keim, “Knowledge Generation Model for Visual Analytics,” IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 1604–1613.
S. Sarwar, A. Dutta, C. Li, et al., “Training Artificial Neural Network With a Cultural Algorithm,” Neural Processing Letters 53, no. 6 (2021): 4637–4674, https://doi.org/10.1007/s11063‐024‐11636‐7.
Z. Chen , Y. Wang, Q. Wang, S. Liu, and H. Qu , “Visual Abstraction and Exploration of Multi‐Class Scatterplots,” IEEE Transactions on Visualization and Computer Graphics 26, no. 6 (2020): 2291–2304.
A. Endert, W. Ribarsky, C. Turkay, et al., “The State of the Art in Integrating Machine Learning Into Visual Analytics,” Computer Graphics Forum 36, no. 8 (2017): 458–486.
M. Liu , J. Shi , Z. Li , C. Li , J. Zhu , and S. Liu, “Towards Better Analysis of Deep Convolutional Neural Networks,” IEEE Transactions on Visualization and Computer Graphics 23, no. 1 (2017): 91–100.
K. Kurzhals and D. Weiskopf, “Space‐Time Visual Analytics of Eye‐Tracking Data for Dynamic Stimuli,” IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (2013): 2129–2138.
T. Blascheck, K. Kurzhals, M. Raschke, M. Burch, D. Weiskopf, and T. Ertl, “Visualization of Eye Tracking Data: A Taxonomy and Survey,” Computer Graphics Forum 36, no. 8 (2017): 260–284.
Y. Zou, S. Pintong, T. Shen, and D. B. Luh, “Cross‐Cultural Design in Costume: Case Study on Totemic Symbols of China and Thailand,” Humanities and Social Sciences Communications 11, no. 1 (2024): 1573, https://doi.org/10.1057/s41599‐024‐03707‐w.
Grant Information:
2025JGA396 2025 Guangxi Higher Education Undergraduate Teaching Reform Project "Construction and Practice of the 'Dual-Three-Stage-Four-Integration' Teaching Model for the 'Brand Identity Design' Course from the Perspective of Artificial Intelligence"; Education Department of Guangxi Zhuang Autonomous Region 2025 Guangxi Higher Education Undergraduate Teaching Reform Project "Construction and Practice of the 'Dual-Three-Stage-Four-Integration' Teaching Model for the 'Brand Identity Design' Course from the Perspective of Artificial Intelligence"
Contributed Indexing:
Keywords: artificial intelligence; cross‐cultural design; cultural adaptation; interactive visualization; machine learning; user experience
Entry Date(s):
Date Created: 20260206 Date Completed: 20260206 Latest Revision: 20260206
Update Code:
20260207
DOI:
10.1111/nyas.70198
PMID:
41649874
Database:
MEDLINE
*Further Information*
*Current interactive information visualization systems often fail to adequately incorporate cultural elements, limiting their effectiveness in cross-cultural communication and reducing user engagement across diverse global audiences. This research presents a comprehensive methodology that integrates artificial intelligence technologies with cultural elements to create more engaging and culturally appropriate interactive visualization systems. The proposed approach includes an intelligent cultural element identification algorithm achieving 91.2% recognition accuracy, an adaptive visualization generation framework that automatically incorporates cultural preferences, and real-time interface optimization mechanisms that dynamically adjust to user behavior patterns. Experimental validation with 600 participants from diverse cultural backgrounds demonstrated significant improvements: 23.5% increase in user satisfaction, 18.3% reduction in cognitive load, and 31.7% enhancement in user engagement compared to traditional visualization approaches. The multidimensional evaluation revealed superior performance across aesthetic appeal, functional usability, and cultural appropriateness metrics. This research contributes theoretical frameworks for understanding cultural influences on visualization perception and provides practical guidelines for implementing culturally informed, AI-driven design systems in multicultural environments.
(© 2026 The New York Academy of Sciences.)*