A COMPREHENSIVE REVIEW OF GENERATIVE ARTIFICIAL INTELLIGENCE APPLICATIONS IN DATA VISUALIZATION - Luong Thi Minh Hue*, Nguyen The Vinh, Nguyen Van Viet, Nguyen Huu Khanh, Nguyen Kim Son, Duong Thuy Huong (Tạp chí Khoa học)
Using natural language to generate visual representations of data (NL2VIS) is emerging as a promising research direction, driven by the rapid development of Generative AI (GenAI). Given the growing number of studies in this area, this paper conducts a systematic review using the PRISMA method. Based on the analysis of 46 papers published between 2018 and 2024, the findings indicate that: 1) The number of publications on this topic is increasing, with the IEEE Transactions on Visualization and Computer Graphics being the primary outlet; the Generative Adversarial Network (GAN) model serves as a foundational technology; 2) Research primarily focuses on integrating large language models (LLMs), such as ChatGPT, and enhancing the accuracy and interpretability of AI systems; 3) Current challenges include the lack of high-quality training data, limited transparency and interpretability, and ethical concerns related to the application of GenAI. Future research should aim to improve generative AI models, design user-centred interfaces, and address issues such as bias, trust, and privacy in AI- generated data visualizations.