Article: Advancing the application of intelligent design systems in adaptive co-creation models using AIGC and reinforcement learning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.59 pp.1 - 24 Abstract: In current intelligent design systems, text prompt word optimisation is a key challenge to improve the quality of image generation. Aiming at the problems of uncertain direction of prompt words and difficult quality evaluation, this paper develops an adaptive co-creation based on AIGC and reinforcement learning. The model adopts a three-stage training framework: first, supervised fine-tuning of the mapping relationship of prompt word pairs is performed, then multi-modal visual feedback of PickScore and aesthetic value models is fused through reward modelling, and finally reinforcement learning is used to fine-tune and optimise the generation strategy. The model performs well in many indexes. In the performance test, the FID of the model decreases to 15.2 ± 1. 5 and the IS increases to 8.9 ± 0. 6. In the robustness test, the FID of the model is 17.5 ± 1. 7 under the noise prompt. In addition, in the practical test, the overall user satisfaction reaches 4.4 ± 0. 3. These results show that the model realises the collaborative optimisation of prompt words and image generation through adaptive mechanism, and provides an efficient end-to-end optimisation scheme. However, the model still needs to be further refined. Therefore, in future research directions, it is necessary to focus on reducing complexity and enhancing real-time feedback integration. Inderscience Publishers - linking academia, business and industry through research

Title: Advancing the application of intelligent design systems in adaptive co-creation models using AIGC and reinforcement learning

Authors: Tian Liu

Addresses: Design School, Guangzhou Maritime University, GuangZhou, 510000, China

Abstract: In current intelligent design systems, text prompt word optimisation is a key challenge to improve the quality of image generation. Aiming at the problems of uncertain direction of prompt words and difficult quality evaluation, this paper develops an adaptive co-creation based on AIGC and reinforcement learning. The model adopts a three-stage training framework: first, supervised fine-tuning of the mapping relationship of prompt word pairs is performed, then multi-modal visual feedback of PickScore and aesthetic value models is fused through reward modelling, and finally reinforcement learning is used to fine-tune and optimise the generation strategy. The model performs well in many indexes. In the performance test, the FID of the model decreases to 15.2 ± 1. 5 and the IS increases to 8.9 ± 0. 6. In the robustness test, the FID of the model is 17.5 ± 1. 7 under the noise prompt. In addition, in the practical test, the overall user satisfaction reaches 4.4 ± 0. 3. These results show that the model realises the collaborative optimisation of prompt words and image generation through adaptive mechanism, and provides an efficient end-to-end optimisation scheme. However, the model still needs to be further refined. Therefore, in future research directions, it is necessary to focus on reducing complexity and enhancing real-time feedback integration.

Keywords: artificial intelligence generated content; AIGC; reinforcement learning; adaptive; co-creation model.

DOI: 10.1504/IJICT.2026.153929

International Journal of Information and Communication Technology, 2026 Vol.27 No.59, pp.1 - 24

Received: 09 Dec 2025
Accepted: 30 Jan 2026
Published online: 08 Jun 2026 *

Title: Advancing the application of intelligent design systems in adaptive co-creation models using AIGC and reinforcement learning

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