Article: Explainable AI-assisted creative system for visual communication design: based on diffusion models and user intent understanding Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.63 pp.1 - 32 Abstract: At present, visual communication design AI assistant tools have problems such as user intention understanding deviation, unexplainable generated results, fragmented interaction and low cross-module collaboration efficiency. This paper proposes an interpretable AI assistant creative system integrating diffusion model, user intention understanding and advanced communication technology. Its core is DesignXAI with the logic of intent transfer-controllable generation-process interpretation-collaborative optimisation. Experiments show the system outperforms mainstream models, with 91% intention understanding accuracy, 89.3 user satisfaction and 0.87 intention-result semantic consistency, providing an efficient intelligent auxiliary method. Inderscience Publishers - linking academia, business and industry through research

Title: Explainable AI-assisted creative system for visual communication design: based on diffusion models and user intent understanding

Authors: Jue Wang; Wu Song

Addresses: School of Humanities and Arts, Changde College, Changde, 415000, China ' School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, Hunan – 415000, China

Abstract: At present, visual communication design AI assistant tools have problems such as user intention understanding deviation, unexplainable generated results, fragmented interaction and low cross-module collaboration efficiency. This paper proposes an interpretable AI assistant creative system integrating diffusion model, user intention understanding and advanced communication technology. Its core is DesignXAI with the logic of intent transfer-controllable generation-process interpretation-collaborative optimisation. Experiments show the system outperforms mainstream models, with 91% intention understanding accuracy, 89.3 user satisfaction and 0.87 intention-result semantic consistency, providing an efficient intelligent auxiliary method.

Keywords: visual communication design; explainable AI; diffusion model; user intent understanding; semantic communication; edge computing.

DOI: 10.1504/IJICT.2026.154110

International Journal of Information and Communication Technology, 2026 Vol.27 No.63, pp.1 - 32

Received: 20 Jan 2026
Accepted: 24 Mar 2026
Published online: 12 Jun 2026 *

Title: Explainable AI-assisted creative system for visual communication design: based on diffusion models and user intent understanding

Keep up-to-date