Show Me What You Mean: Inclusive Augmented Typography for Students with Dyslexia

Darren Taljaard, Myra Thiessen

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Augmenting the visual appearance of continuous text may contribute to more inclusive and effective learning opportunities for university students with dyslexia (SwD). This neurodiverse population remains largely reliant on reading tools developed for “typical” readers. Although SwD find reading slower, more tiring, and more difficult, they are also known to use deep learning approaches, which may be assisted by inclusive, custom typographic and layout systems. While printed texts offer only one typographic presentation and make limited use of visual cues, the affordances of digital reading tools could result in multiple visual adaptations to suit individual needs, preferences, and reading strategies. This could be achieved with networked devices using artificial intelligence (AI) to read the content in texts, and by applying typography and layout modifications in response. A human-centered, ethically informed approach is required to conceptualize and design inclusive reading systems of this sort. This paper identifies and explores key ethical questions and practical implications raised by the hypothesis that incorporating AI into reading tools and visually adapting texts could facilitate more inclusive reading and learning experiences, and better meet the educational requirements of SwD.
Original languageEnglish
Pages (from-to)53-74
Number of pages22
JournalVisible Language
Volume57
Issue number1
Publication statusPublished - 2023

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