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LaMPost: Evaluation of an AI-assisted Writing Email Editor Prototype for Adults with Dyslexia

Steven Goodman
Erin Buehler
Patrick Clary
Andy Coenen
Aaron Michael Donsbach
Tiffanie Horne
Bob MacDonald
Rain Breaw Michaels
Ajit Narayanan
Joel Christopher Riley
Alex Santana
Rachel Sweeney
Phil Weaver
Ann Yuan
Meredith Ringel Morris
Proceedings of ASSETS 2022, ACM (2022) (to appear)

Abstract

Prior work has explored the writing challenges experienced by people with dyslexia, and the potential for new spelling, grammar, and word retrieval technologies to address these challenges. However, the capabilities for natural language generation demonstrated by the latest class of large language models (LLMs) highlight an opportunity to explore new forms of human-AI writing support tools. In this paper, we introduce LaMPost, a prototype email-writing interface that explores the potential for LLMs to power writing support tools that address the varied needs of people with dyslexia. LaMPost draws from our understanding of these needs and introduces novel AI-powered features for email-writing, including: outlining main ideas, generating a subject line, suggesting changes, rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying many promising routes for further exploration (including the popularity of the “rewrite” and “subject line” features), but also finding that the current generation of LLMs may not surpass the accuracy and quality thresholds required to meet the needs of writers with dyslexia. Surprisingly, we found that participants’ awareness of the AI had no effect on their perception of the system, nor on their feelings of autonomy, expression, and self-efficacy when writing emails. Our findings yield further insight into the benefits and drawbacks of using LLMs as writing support for adults with dyslexia and provide a foundation to build upon in future research.