Abstract
Text-to-image generation artificial intelligence (T2I GenAI) tools such as DALL·E, Stable Diffusion, and Midjourney have emerged as fast and flexible tools for creating scientific visualisations since 2020. However, their application in scientific communication and collaboration remains fragmented across disciplines. To map this landscape, we conducted a scoping review of 38 peer-reviewed articles published after 2020, following the Arksey & O’Malley framework and PRISMA-ScR guidelines. We combined manual searches in Web of Science, Scopus, ACM, and IEEE with AI-assisted searches through Elicit. Then we applied strict inclusion criteria and subject coding. Our analysis identified four main mechanisms by which T2I GenAI enhances scientific communication and collaboration: accelerated visualisation, generation of otherwise unattainable images, prompt-based shared vocabularies, and improved accessibility. We also documented prevalent challenges, including dataset bias, opacity of model processes, ethical and copyright issues, and frequent mismatches between user intent and AI output. Finally, we propose the current landscape and future research recommendations for utilising AI in scientific contexts. This review provides a structured overview for researchers and practitioners seeking to utilise these tools to enhance scientific communication and collaboration.
Keywords
Text-to-Image Gen AI; Scientific visualization; Science communication; Scientific
DOI
https://doi.org/10.21606/iasdr.2025.1007
Citation
Tu, H.W., Mesa, D.,and Thong, C.(2025) Scientific visual communication and collaboration within text-to-image GenAI: A Scoping Review, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.1007
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
Scientific visual communication and collaboration within text-to-image GenAI: A Scoping Review
Text-to-image generation artificial intelligence (T2I GenAI) tools such as DALL·E, Stable Diffusion, and Midjourney have emerged as fast and flexible tools for creating scientific visualisations since 2020. However, their application in scientific communication and collaboration remains fragmented across disciplines. To map this landscape, we conducted a scoping review of 38 peer-reviewed articles published after 2020, following the Arksey & O’Malley framework and PRISMA-ScR guidelines. We combined manual searches in Web of Science, Scopus, ACM, and IEEE with AI-assisted searches through Elicit. Then we applied strict inclusion criteria and subject coding. Our analysis identified four main mechanisms by which T2I GenAI enhances scientific communication and collaboration: accelerated visualisation, generation of otherwise unattainable images, prompt-based shared vocabularies, and improved accessibility. We also documented prevalent challenges, including dataset bias, opacity of model processes, ethical and copyright issues, and frequent mismatches between user intent and AI output. Finally, we propose the current landscape and future research recommendations for utilising AI in scientific contexts. This review provides a structured overview for researchers and practitioners seeking to utilise these tools to enhance scientific communication and collaboration.