Image-to-UI Accessibility: Assessing ChatGPT’s Effectiveness in Generating Accessible Android Screens from Figma Templates
Abstract
As accessibility remains a persistent challenge in mobile applications, recent advances in Large Language Models (LLMs) offer promising support for addressing these issues in the development process. This study investigates the potential of ChatGPT-4 to generate accessible Android user interfaces directly from screen mockups. Using a dataset of 60 screens in 8 Figma-based UI templates, we prompted ChatGPT to produce Jetpack Compose code solely from screen images. We then evaluated the generated code using Google’s Accessibility Scanner, identifying a total of 302 accessibility violations. Through iterative prompting guided by the scanner’s feedback, we achieved a reduction of more than 50% in the reported issues. However, the process also introduced new violations in some cases, highlighting important limitations in the consistency and reliability of the model. Our findings suggest that while LLMs like ChatGPT can support accessibility improvements in mobile User Interface (UI) development, human oversight remains essential. This study contributes to ongoing discussions on the role of AI in inclusive software design and the future of image-to-code generation pipelines.
Keywords:
Large Language Models, Mobile Accessibility, Jetpack Compose, Figma Templates, ChatGPT-4
References
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Matheus Lima, Guido Lemos, and Raoni Kulesza. 2021. A Proposal for an Accessible Videoconferencing Interface for People with Hearing Impairments. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 29–36.
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Ribamar Martins, Daniel Rabelo, Maria Araújo, and Windson de Carvalho. 2022. Where is the description? Investigating accessibility issues in portuguese versions of smart home apps. In Anais do XXI Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (Diamantina). SBC, Porto Alegre, RS, Brasil. [link]
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Steven I. Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D. Weisz. 2023. The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development. In Proceedings of the 28th International Conference on Intelligent User Interfaces (Sydney, NSW, Australia) (IUI ’23). Association for Computing Machinery, New York, NY, USA, 491–514. DOI: 10.1145/3581641.3584037
Sarang Shaikh, Sule Yildirim Yayilgan, Blanka Klimova, and Marcel Pikhart. 2023. Assessing the Usability of ChatGPT for Formal English Language Learning. European Journal of Investigation in Health, Psychology and Education 13 (09 2023), 1937–1960. DOI: 10.3390/ejihpe13090140
Hyunjae Suh, Mahan Tafreshipour, Sam Malek, and Iftekhar Ahmed. 2025. Human or LLM? A Comparative Study on Accessible Code Generation Capability. arXiv:2503.15885 [cs.SE] [link]
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Hussam Alkaissi and Samy I. McFarlane. 2023. Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus 15, 2 (2023), e35179. DOI: 10.7759/cureus.35179
Abdulaziz Alshayban, Iftekhar Ahmed, and Sam Malek. 2020. Accessibility issues in Android apps: state of affairs, sentiments, and ways forward. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE ’20). Association for Computing Machinery, New York, NY, USA, 1323–1334. DOI: 10.1145/3377811.3380392
Matheus Andrade, Daniel Rabelo, Ribamar Martins, and Windson Viana. 2024. Investigating the accessibility of popular mobile Android apps: a prevalence, category, and language study. In Proceedings of the 30th Brazilian Symposium on Multimedia and the Web (Juiz de Fora/MG). SBC, Porto Alegre, RS, Brasil, 400–404. DOI: 10.5753/webmedia.2024.242041
Andreas Biørn-Hansen, Christoph Rieger, Tor-Morten Grønli, Tim A. Majchrzak, and Gheorghita Ghinea. 2020. An Empirical Investigation of Performance Overhead in Cross-Platform Mobile Development Frameworks. Empirical Software Engineering 25, 4 (2020), 2997–3040. DOI: 10.1007/s10664-020-09827-6
Brasil. 2019. Acessibilidade Digital. [link]
Sen Chen, Chunyang Chen, Lingling Fan, Mingming Fan, Xian Zhan, and Yang Liu. 2022. Accessible or Not? An Empirical Investigation of Android App Accessibility. arXiv:2203.06422 [cs.SE] [link]
Yunnong Chen, Shixian Ding, YingYing Zhang, Wenkai Chen, Jinzhou Du, Lingyun Sun, and Liuqing Chen. 2025. DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models. arXiv:2506.13663 [cs.SE] [link]
Jingwen Cheng, Kshitish Ghate, Wenyue Hua, William Yang Wang, Hong Shen, and Fei Fang. 2025. REALM: A Dataset of Real-World LLM Use Cases. arXiv:2503.18792 [cs.HC] [link]
Cole Stryker and Jim Holdsworth, IBM. 2024. What Is NLP (Natural Language Processing)? IBM. [link] Accessed: 2025-07-07.
Daniel de Sousa Moraes, Polyana Bezerra da Costa, Antonio JG Busson, José Matheus Carvalho Boaro, Carlos de Salles Soares Neto, and Sergio Colcher. 2023. On the Challenges of Using Large Language Models for NCL Code Generation. In Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia). SBC, 151–156.
Giovanni Delnevo, Manuel Andruccioli, and Silvia Mirri. 2024. On the Interaction with Large Language Models for Web Accessibility: Implications and Challenges. In 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC). 1–6. DOI: 10.1109/CCNC51664.2024.10454680
Xueying Du, Mingwei Liu, KaixinWang, HanlinWang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2024. Evaluating Large Language Models in Class-Level Code Generation. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (Lisbon, Portugal) (ICSE ’24). Association for Computing Machinery, New York, NY, USA, Article 81, 13 pages. DOI: 10.1145/3597503.3639219
Reinaldo Ferraz, Ana Duarte, João Bárbara, Adriano CM Pereira, and Wagner Meira. 2023. A platform to check website compliance with web accessibility standards. In Proceedings of the 20th International Web for All Conference. 75–78.
Ana Cristina Bicharra Garcia. 2020. Ética e Inteligência Artificial. Computação Brasil 43 (nov. 2020), 14–22. DOI: 10.5753/compbr.2020.43.1791
Google. 2024. Principles for Improving App Accessibility. Google. [link]
Google. 2024. Why adopt Compose. Google. [link]
Google. 2025. Accessibility in Jetpack Compose. Google. [link]
Google LLC. 2025. Get started with Accessibility Scanner. Google. [link] Accessed: 2025-07-07.
Marianna Di Gregorio, Dario Di Nucci, Fabio Palomba, and Giuliana Vitiello. 2022. The making of accessible Android applications: an empirical study on the state of the practice. Empirical Software Engineering 27, 6 (2022), 145. DOI: 10.1007/s10664-022-10182-x Open access under CC BY 4.0.
Kei Koyanagi, Dong Wang, Kotaro Noguchi, Masanari Kondo, Alexander Serebrenik, Yasutaka Kamei, and Naoyasu Ubayashi. 2024. Exploring the Effect of Multiple Natural Languages on Code Suggestion Using GitHub Copilot. In 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR). IEEE, 481–486.
Elmar Krainz, Klaus Miesenberger, and Johannes Feiner. 2018. Can We Improve App Accessibility with Advanced Development Methods?. In Computers Helping People with Special Needs: 16th International Conference, ICCHP 2018, Linz, Austria, July 11-13, 2018, Proceedings, Part I (Linz, Austria). Springer-Verlag, Berlin, Heidelberg, 64–70. DOI: 10.1007/978-3-319-94277-3_12
Elsa Lassfolk. 2023. User Experience App Design for Visually Impaired Elderly. Technical Report. Metropolia University of Applied Sciences, Helsinki, Finland.
Manoel Victor Rodrigues Leite, Lilian Passos Scatalon, André Pimenta Freire, and Marcelo Medeiros Eler. 2021. Accessibility in the mobile development industry in Brazil: Awareness, knowledge, adoption, motivations and barriers. Journal of Systems and Software 177 (2021), 110942. DOI: 10.1016/j.jss.2021.110942
Guilherme Lima, Rodrigo Costa, and Marcio Moreno. 2019. Semantics and multimedia: an introduction to symbolic artificial intelligence applied to multimedia. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web. 7–9.
Matheus Lima, Guido Lemos, and Raoni Kulesza. 2021. A Proposal for an Accessible Videoconferencing Interface for People with Hearing Impairments. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 29–36.
Yihao Liu, Xu Cao, Tingting Chen, Yankai Jiang, Junjie You, Minghua Wu, Xiaosong Wang, Mengling Feng, Yaochu Jin, and Jintai Chen. 2025. From screens to scenes: A survey of embodied AI in healthcare. Information Fusion 119 (July 2025), 103033. DOI: 10.1016/j.inffus.2025.103033
Juan-Miguel López-Gil and Juanan Pereira. 2024. Turning manual web accessibility success criteria into automatic: an LLM-based approach. Universal Access in the Information Society 24 (2024), 837–852. DOI: 10.1007/s10209-024-01108-z
Ribamar Martins, Daniel Rabelo, Maria Araújo, and Windson de Carvalho. 2022. Where is the description? Investigating accessibility issues in portuguese versions of smart home apps. In Anais do XXI Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (Diamantina). SBC, Porto Alegre, RS, Brasil. [link]
Júlia Holanda Muniz, Daniel Mesquita Feijó Rabelo, and Windson Viana. 2024. Assessing Accessibility Levels in Mobile Applications Developed from Figma Templates. In Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (Crete, Greece) (PETRA ’24). Association for Computing Machinery, New York, NY, USA, 316–321. DOI: 10.1145/3652037.3652075
Alberto Dumont Alves Oliveira, Paulo Sérgio Henrique Dos Santos, Wilson Estécio Marcílio Júnior, Wajdi M Aljedaani, Danilo Medeiros Eler, and Marcelo Medeiros Eler. 2023. Analyzing Accessibility Reviews Associated with Visual Disabilities or Eye Conditions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 37, 14 pages. DOI: 10.1145/3544548.3581315
OpenAI. 2023. GPT-4 Technical Report. [link]. Accessed: 2025-07-22.
Achraf Othman, Amira Dhouib, and Aljazi Nasser Al Jabor. 2023. Fostering websites accessibility: A case study on the use of the Large Language Models ChatGPT for automatic remediation. In Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments (Corfu, Greece) (PETRA ’23). Association for Computing Machinery, New York, NY, USA, 707–713. DOI: 10.1145/3594806.3596542
Seokhyeon Park, Yumin Song, Soohyun Lee, Jaeyoung Kim, and Jinwook Seo. 2025. Leveraging Multimodal LLM for Inspirational User Interface Search. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). ACM, 1–22. DOI: 10.1145/3706598.3714213
Daniel Mesquita Feijo Rabelo, Ribamar de Souza Martins, Isaac Santos, Paulo Henrique G. Da Silva, Kiev Gama, and Windson Viana. 2025. Breaking Barriers in Mobile Accessibility: A Study of LLM-Generated Native Android Interfaces . In 2025 IEEE/ACM 12th International Conference on Mobile Software Engineering and Systems (MOBILESoft). IEEE Computer Society, Los Alamitos, CA, USA, 20–31. DOI: 10.1109/MOBILESoft66462.2025.00010
Anatália Saraiva Martins Ramos. 2023. Inteligência Artificial Generativa baseada em grandes modelos de linguagem - ferramentas de uso na pesquisa acadêmica. SciELO Preprints (may 2023). DOI: 10.1590/SciELOPreprints.6105
Anne Spencer Ross, Xiaoyi Zhang, James Fogarty, and Jacob O. Wobbrock. 2020. An Epidemiology-inspired Large-scale Analysis of Android App Accessibility. ACM Trans. Access. Comput. 13, 1, Article 4 (April 2020), 36 pages. DOI: 10.1145/3348797
Steven I. Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D. Weisz. 2023. The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development. In Proceedings of the 28th International Conference on Intelligent User Interfaces (Sydney, NSW, Australia) (IUI ’23). Association for Computing Machinery, New York, NY, USA, 491–514. DOI: 10.1145/3581641.3584037
Sarang Shaikh, Sule Yildirim Yayilgan, Blanka Klimova, and Marcel Pikhart. 2023. Assessing the Usability of ChatGPT for Formal English Language Learning. European Journal of Investigation in Health, Psychology and Education 13 (09 2023), 1937–1960. DOI: 10.3390/ejihpe13090140
Hyunjae Suh, Mahan Tafreshipour, Sam Malek, and Iftekhar Ahmed. 2025. Human or LLM? A Comparative Study on Accessible Code Generation Capability. arXiv:2503.15885 [cs.SE] [link]
Michael Cheng-Tek Tai. 2020. The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal 32, 4 (Oct–Dec 2020), 339–343. DOI: 10.4103/tcmj.tcmj_71_20
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.
Christopher Vendome, Diana Solano, Santiago Liñán, and Mario Linares-Vásquez. 2019. Can Everyone use my app? An Empirical Study on Accessibility in Android Apps. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). 41–52. DOI: 10.1109/ICSME.2019.00014
Chaozheng Wang, Zongjie Li, Cuiyun Gao, Wenxuan Wang, Ting Peng, Hailiang Huang, Yuetang Deng, Shuai Wang, and Michael R. Lyu. 2024. Exploring Multi-Lingual Bias of Large Code Models in Code Generation. arXiv:2404.19368 [cs.SE] [link]
Fei Wang, Kamakshi Kodur, Michael Micheletti, Shu-Wei Cheng, Yogalakshmi Sadasivam, Yue Hu, and Zening Li. 2024. Large Language Model Driven Automated Software Application Testing. Technical Disclosure Commons. [link]
World Health Organization. 2023. Blindness and Visual Impairment. World Health Organization. [link]
World Health Organization. 2023. Disability. World Health Organization. [link]
World Wide Web Consortium (W3C). 2023. Mobile Accessibility at W3C. World Wide Web Consortium (W3C). [link] Accessed: 2025-07-07.
World Wide Web Consortium (W3C). 2025. Web Content Accessibility Guidelines (WCAG) 2.1. World Wide Web Consortium (W3C). [link] Accessed: 2025-07-07.
Zhuolin Xu, Qiushi Li, and Shin Hwei Tan. 2025. Guiding ChatGPT to Fix Web UI Tests via Explanation-Consistency Checking. arXiv:2312.05778 [cs.SE] [link]
Shunguo Yan and P. G. Ramachandran. 2019. The Current Status of Accessibility in Mobile Apps. ACM Trans. Access. Comput. 12, 1, Article 3 (Feb. 2019), 31 pages. DOI: 10.1145/3300176
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Published
2025-11-10
How to Cite
XAVIER, Lucas Emanuel Oliveira; RABELO, Daniel Mesquita Feijó; MARTINS, Ribamar de Souza; VIANA, Windson.
Image-to-UI Accessibility: Assessing ChatGPT’s Effectiveness in Generating Accessible Android Screens from Figma Templates. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 302-311.
DOI: https://doi.org/10.5753/webmedia.2025.15298.
