Article: Generative adversarial network-driven interactive simulation modelling for environmental design Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.3 pp.36 - 52 Abstract: This paper presents a cognitive-semantic guided generative adversarial network for automatically generating interactive environment layouts that optimise both visual realism and user experience. By computationally operationalising cognitive load theory, our framework integrates a novel interaction-aware discriminator and a semantic consistency loss, enabling the generator to produce layouts that minimise navigational cognitive load. Validated on the Stanford 2D-3D-Semantics dataset, our model significantly outperforms state-of-the-art methods in functional metrics, achieving an 85.2% navigation success rate, a 13.4% higher mean intersection over union than graph-based methods (68.7% versus 55.2%), and a substantially lower cognitive load score of 0.65. Ablation studies and user evaluations involving 45 participants confirm the necessity of each component and demonstrate a strong preference for the generated environments. This work aims to establish between cognitive theory and generative artificial intelligence for human-centric design. Inderscience Publishers - linking academia, business and industry through research

Title: Generative adversarial network-driven interactive simulation modelling for environmental design

Authors: Jinqi Wang; Zhuo Fan

Addresses: College of Art and Design, Nanning University, Nanning, 530200, China ' College of Art and Design, Nanning University, Nanning, 530200, China

Abstract: This paper presents a cognitive-semantic guided generative adversarial network for automatically generating interactive environment layouts that optimise both visual realism and user experience. By computationally operationalising cognitive load theory, our framework integrates a novel interaction-aware discriminator and a semantic consistency loss, enabling the generator to produce layouts that minimise navigational cognitive load. Validated on the Stanford 2D-3D-Semantics dataset, our model significantly outperforms state-of-the-art methods in functional metrics, achieving an 85.2% navigation success rate, a 13.4% higher mean intersection over union than graph-based methods (68.7% versus 55.2%), and a substantially lower cognitive load score of 0.65. Ablation studies and user evaluations involving 45 participants confirm the necessity of each component and demonstrate a strong preference for the generated environments. This work aims to establish between cognitive theory and generative artificial intelligence for human-centric design.

Keywords: cognitive load theory; CLT; generative adversarial networks; GANs; interactive environment design; semantic scene understanding; human navigation simulation.

DOI: 10.1504/IJICT.2026.151493

International Journal of Information and Communication Technology, 2026 Vol.27 No.3, pp.36 - 52

Received: 13 Oct 2025
Accepted: 08 Nov 2025
Published online: 02 Feb 2026 *

Title: Generative adversarial network-driven interactive simulation modelling for environmental design

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