Advancing Accessibility and Social Inclusion through Generative AI and Machine Learning-Powered Multi-Modality on Mobile Platforms
DOI:
https://doi.org/10.32628/CSEIT25111254Keywords:
Multi-Modal Accessibility, Generative AI, Digital Inclusion, Assistive Technology, Mobile ComputingAbstract
The advent of generative AI-powered multi-modality on mobile platforms is transforming how devices function as empowering tools, facilitating inclusive and accessible experiences for diverse populations. By integrating voice, text, and image interactions with advanced AI technologies such as Whisper, Gemini, alongside mobile-specific frameworks, multi-modality addresses significant accessibility challenges in mobile computing. This integration enables features like voice-to-text conversion, image-based form filling, and contextual conversation summarization, which enhance usability for individuals with disabilities, non-native speakers, and those managing complex digital workflows. Although acknowledging several advancements, the article also addresses challenges such as data privacy, algorithmic bias, and unequal distribution of AI technologies, exploring both societal and technical difficulties in their equitable deployment. Additionally, it discusses multi-modality application and benefits across healthcare, education, and public services, calling for a collaborative and inclusive approach in technology development to ensure that innovations benefit all users. This analysis highlights how generative AI and multi-modality are key to advancing digital inclusivity on mobile platforms.
Downloads
References
World Bank Group, "Digital Development Global Practice," World Bank, 2023. [Online]. Available: https://thedocs.worldbank.org/en/doc/b16e2ba1cb754ab47a2dd1b214dd374e-0400062023/original/DigitalDevelopmentBrochure.pdf
World Bank Group, "World Report on Disability," World Bank, 2011. [Online]. Available: https://documents1.worldbank.org/curated/ar/665131468331271288/pdf/627830WP0World00PUBLIC00BOX361491B0.pdf
UNICEF and WHO, "Global report on assistive technology," UNICEF, 2022. [Online]. Available: https://www.unicef.org/reports/global-report-assistive-technology
Ana Maria Carrillo Soubic, "Background Paper on Technologies and Older Persons," United Nations Department of Economic and Social Affairs, 2021. [Online]. Available: https://social.desa.un.org/sites/default/files/inline-files/Technologies-and-older-persons-by-Ana-Maria-Carrillo-Soubic.pdf
UNESCO, "Artificial intelligence and gender equality: key findings of UNESCO’s Global Dialogue," UNESCO Digital Library, 2020. [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000374174
International Telecommunication Union, "Measuring digital development: Facts and Figures 2024," ITU Publications, 2024. [Online]. Available: https://www.itu.int/en/ITU-D/Statistics/pages/facts/default.aspx
W3C Web Accessibility Initiative (WAI), "Making the Web Accessible," W3C. [Online]. Available: https://www.w3.org/WAI/
OECD, "State of implementation of the OECD AI Principles," OECD Digital Economy Papers, June 2021. [Online]. Available: https://www.oecd.org/en/publications/state-of-implementation-of-the-oecd-ai-principles_1cd40c44-en.html
Tim Springer, "The Fifth Annual State of Digital Accessibility Report," Level Access, 2023. [Online]. Available: https://www.levelaccess.com/wp-content/uploads/2023/12/The-Fifth-Annual-State-of-Digital-Accessibility-Report.pdf
N. Chumuang and M. Ketcham, "Model for Handwritten Recognition Based on Artificial Intelligence," 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Pattaya, Thailand, 2018, pp. 1-5, doi: 10.1109/iSAI-NLP.2018.8692958. DOI: https://doi.org/10.1109/iSAI-NLP.2018.8692958
FXM, "AI in Education: Enhancing Accessibility and Inclusivity for Individuals with Disabilities," FXM Web, Aug. 2024. [Online]. Available: https://www.fxmweb.com/insights/ai-in-education-enhancing-accessibility-and-inclusivity-for-individuals-with-disabilities.html
Daniel Gaspar-Figueiredo et al., "Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces," arXiv:2405.09255v1 [cs.HC], May 2024. Available: https://arxiv.org/html/2405.09255v1
"IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Multimodal Conversion Version 1.2," in IEEE Std 3300-2022 , vol., no., pp.1-108, 28 April 2023, doi: 10.1109/IEEESTD.2023.10112603. DOI: https://doi.org/10.1109/IEEESTD.2023.10112603
Syed, W. (2025). AI-powered multi-modal form filling: Advancing accessibility through voice and image recognition. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1–11. https://doi.org/10.32628/CSEIT2511120301. DOI: https://doi.org/10.32628/CSEIT25111203
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
