Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharing

2026-01-18

AI Summary

This study addresses the challenges generative AI poses in faithfully representing the lived experiences and identities of disabled individuals when supporting their creation of personal narratives, often due to bias and expressive distortion. Grounded in digital storytelling theory, the research collaborates with nine disabled participants to propose a “Critical Moment Rendering” analytical framework. This framework systematically examines participants’ motivations, practices, and experiences in using generative AI for video creation through four dimensions: the re-presentation of elusive scenes, the negotiation of identity concealment and disclosure, contextual coherence, and emotional expression. The findings yield design implications for AI systems tailored to disabled storytelling, emphasizing narrative integrity, adaptive media formatting, and robust error-correction mechanisms, thereby offering both theoretical and practical foundations for developing more inclusive generative AI tools.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Generative AI (GenAI) is both promising and challenging in supporting people with disabilities (PwDs) in creating stories about disability. GenAI can reduce barriers to media production and inspire the creativity of PwDs, but it may also introduce biases and imperfections that hinder its adoption for personal expression. In this research, we examine how nine PwD from a disability advocacy group used GenAI to create videos sharing their disability experiences. Grounded in digital storytelling theory, we explore the motivations, expression, and sharing of PwD-created GenAI story videos. We conclude with a framework of momentous depiction, which highlights four core affordances of GenAI that either facilitate or require improvements to better support disability storytelling: non-capturable depiction, identity concealment and representation, contextual realism and consistency, and emotional articulation. Based on this framework, we further discuss design implications for GenAI in relation to story completion, media formats, and corrective mechanisms.

Problem

• Generative AI
• Disability Storytelling
• Personal Expression
• Media Production
• Bias in AI

Innovation

• Generative AI
• Disability Storytelling
• Momentous Depiction
• Digital Storytelling
• Inclusive Design