Dou Dizhu For All: An Accessible Card Game for VisuallyImpaired Persons in China Powered by ChatGPT's LanguageModel
Video games have become a dominant form of entertainment in the modern world across different platforms and devices.However, people with diverse abilities often find themselves excluded from playing video games due to accessibility challenges (Yuan et al. 2010).Among people with diverse abilities, blind and visually impaired persons (BVIPs) face the significant barriers due to the visual-centric nature of video gaming.To minimise barriers and provide options for visually impaired players, various efforts have been made by companies such as Sony and Xbox, creating accessible games and providing accessibility guidelines for the industry.Additionally, scholars such as Kulik et al. (2021), Mangiron and Zhang (2016), Porter (2014) and Yuan et al. (2010) have made significant academic contributions in this field.So far in China, however, there have been no studies on game accessibility for VIPs, and the investigation into game accessibility across all domains remains uncharted.This study introduces Dou Dizhu For All, also known as "Fighting the Landlord" or "Fighting the Lord", an adapted version of the traditional Chinese card game Dou Dizhu (no publisher), designed for both BVIPs and sighted players.This study will involve two phases: firstly, the adaptation of the game, focusing on the design of interfaces.This includes implementing accessibility features such as customised Textto-Speech (TTS), audio cues, a voice command system powered by ChatGPT's language model, and the design of the user interface (UI).Secondly, semi-structured interviews with end-users, which is based on Immersion Experience Questionnaire (IEQ) (Jennett et al. 2008).The following outlines the design process and the methodological plan for this study.
Authors
- Yunke Deng
Publication Details
- Published
- 2024-09-30
- DOI
- https://doi.org/10.26503/dl.v2024i2.2341
- Primary Topic
- Technology Use by Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 0.00