Can AI generate images, charts and other visual content in academic writing?

Yes, AI can generate images, charts, and other visual content to support academic writing. These capabilities leverage generative adversarial networks (GANs) and diffusion models for images, and large language models integrated with data analysis tools for charts. This capability necessitates detailed textual prompts describing the desired content, style, and data source for charts. Key considerations include accuracy verification, potential biases inherent in training data, ethical implications regarding copyright and originality (especially for derivative or photorealistic images), and accessibility. Generated visuals must be critically evaluated for appropriateness, factual correctness, and proper contextual integration within the scholarly argument; simply prompting "create a graph" without specific data is insufficient. Generated visuals offer valuable support by rapidly creating concept illustrations, draft figures for hypothesis visualization, or schematic diagrams based on precise descriptions. This enhances communication efficiency and aids accessibility. However, they act strictly as assistants; researchers remain responsible for the final visual's accuracy, data integrity, appropriate attribution of the AI tool used, and its scientific validity within the manuscript. The primary value lies in accelerating early drafting, not replacing scholarly data analysis or critical design oversight.