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Advancing Accessibility and Social Inclusion through Generative AI and Machine Learning-Powered Multi-Modality on Mobile Platforms

Author

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  • Waseem Syed

Abstract

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.

Suggested Citation

  • Waseem Syed, 2025. "Advancing Accessibility and Social Inclusion through Generative AI and Machine Learning-Powered Multi-Modality on Mobile Platforms," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 475-483, February.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:702
    DOI: 10.32628/CSEIT25111254
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25111254
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