Applications of Artificial Intelligence in Art and Design: A Comprehensive Review
Bibliographic record
Abstract
The convergence of artificial intelligence (AI) and creative disciplines has accelerated dramatically since the advent of deep generative models, transforming both artistic production and design methodologies. This paper provides a comprehensive review of AI techniques applied in the domains of visual arts, product design, fashion, and architecture. We discuss foundational models, including convolutional neural networks (CNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and vision– language transformers, along with training pipelines, evaluation metrics, and available datasets. Real-world case studies illustrate how AI enhances ideation, accelerates prototyping, and facilitates interactive co-creation between humans and machines. Ethical considerations such as copyright, dataset bias, and environmental cost are analyzed, alongside regulatory developments including the European Union AI Act. Finally, research challenges and future directions are explored, emphasizing explainable creativity, multimodal integration, and sustainable deployment of AI tools in creative industries. This survey aims to provide both a technical foundation and an application-oriented perspective for researchers and practitioners seeking to leverage AI in artistic and industrial design contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".