Comparative experiments demonstrate enhanced data visualisation and user satisfaction in educational settings, indicating effective tools for data literacy.
This study presents a WebGPU-based visualisation framework aimed at enhancing the processing, rendering, and interactivity of large-scale, multidimensional datasets in educational contexts.By integrating artificial intelligence (AI) tools with advanced visualisation techniques, the framework enables efficient data preprocessing, interpolation, and volume texture generation for seamless web-based visualisation.Utilising datasets from oceanographic simulations and educational performance metrics, the system demonstrates versatility across domains.Comparative experiments show that the WebGPU-based solution significantly outperforms previous WebGL-based implementations, reducing rendering time and increasing frame rates.User surveys report high satisfaction in functionality, personalisation, usability, and compatibility.These findings highlight the potential of AI-driven educational data analytics and visualisation tools to support decision-making, enhance user engagement, and promote data literacy in academic and professional training environments.
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Minjun Xie (2026) studied this question.
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