Blind and Low Vision Users’ Experience with AI-Infused Banking Chatbots: AI-Specific Experience Dimensions and System Usability

Abstract

Artificial intelligence (AI) infused chatbots have the potential to radically transform the user experience with digital banking for blind and low vision (BLV) users. AI chatbots offer a radically different interaction style. A considerable part of the theories and principles developed for conventional interaction styles becomes less directly applicable. There is limited empirical research on BLV users’ experience (UX) with AI-infused banking chatbots. This study aims to explore the AI-specific UX dimensions as well as the conventional UX dimensions of banking chatbots regarding BLV users. The empirical study includes a usability test, a semi-structured interview, and a perceived usability measurement scale. The participants are selected from BLV adults who actively use digital banking services. Findings reveal that perceived intelligence is closely intertwined with accessibility performance. Failures in screen reader compatibility, navigation clarity, state feedback, and table interpretation not only reduced task success but also diminished trust in the system. Text-heavy chatbot structures supported simple, well-defined tasks more effectively, whereas graphical-heavy designs caused navigational breakdowns despite appearing accessible. Moreover, financial interactions were strongly associated with independence, control, and dignity, highlighting the existential significance of accessibility beyond technical compliance. This study aims to contribute to the literature by identifying AI-specific user experience dimensions in the BLV context, and also to provide designers in the digital banking sector with applicable design recommendations for developing better chatbot interactions for BLV users. This paper presents the preliminary findings of a study that is still ongoing.

Keywords: Artificial Intelligence, Chatbots, User Experience, Accessibility, Blind And Low Vision, Digital Banking, Human-centered AI

DOI: 10.54941/ahfe1007295