Type of Publication: Article in Collected Edition

Designing Assistive Technologies for Blind and Visually Impaired: Problem Understanding and Design Objectives

Author(s):

Laufer, Jan; Banh, Leonardo; Schoormann, Thorsten; Strobel, Gero

Editor:

Nature, Springer

Title of Anthology:

21th International Conference on Design Science Research in Information Systems and Technology (DESRIST 2026)

Location(s):

Muenster, Germany

Publication Date:

2026

Digital Object Identifier (DOI):

doi:10.1007/978-3-032-28316-0_7

Link to complete version:

https://rdcu.be/flZHG

Talk associated with this publication:

21th International Conference on Design Science Research in Information Systems and Technology (DESRIST 2026)

Citation:

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Abstract

Disability for blind and visually impaired people (BVIP) is produced by inaccessible, fragmented, and dynamically changing surroundings that constrain safe mobility, information access, and social participation, rather than by impairment alone. While assistive technologies (AT) promise richer environmental interpretation and new forms of digital support, they are typically built for mass markets and thus often fail to align with the context-sensitive barriers and everyday practices of BVIP. This study aims to ground the design of (AI-driven) ATs for BVIP in empirically validated problem understanding and actionable design guidance. We adopt the echeloned Design Science Research (eDSR) approach and conduct 25 semi-structured interviews with BVIP to progress from problem analysis to objectives and requirements definition. Our results synthesize the problem space into seven interrelated problem areas spanning daily life, well-being, public interaction, dependencies, financial aspects, interaction with surroundings, and assistive tools, which together yield a validated problem statement emphasizing the lack of integrated, reliable, context-sensitive support. We derive two design objectives and formulate thirteen design requirements that translate user challenges into concrete guidance for (AI-driven) ATs. We lay the ground for improved ATs for BVIP and inclusive IS design.