@inproceedings{rahman-etal-2026-measuring,
title = "Measuring the Impact of {LLM}-Generated Scientific Summaries for Neurodivergent Readers",
author = "Rahman, Abraham Ur and
Koba, Skyler and
Michal, Audrey and
Wilson, Steven R",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.26/",
pages = "298--311",
ISBN = "979-8-89176-495-8",
abstract = "Language technologies increasingly mediate access to scientific information, yet their impact depends on whether they improve real reading experiences for diverse audiences. We study large language model (LLM)-generated scientific summaries as an accessibility intervention for neurodivergent adults, focusing on readers with ADHD and/or autism. Using a mixed-methods design, we first conducted a formative qualitative study with 10 neurodivergent adults to identify design needs. We then ran a quantitative experiment with 200 neurodivergent adults comparing five summary styles across lower- and higher-complexity scientific texts, measuring comprehension, trust, engagement, and cognitive load. Results show that writing-style effects depend on text complexity: structured LLM-generated summaries improved comprehension for higher-complexity articles but reduced comprehension for lower-complexity articles relative to a baseline human-generated extractive summary. Trust increased for simpler LLM summary styles, while engagement and cognitive load showed fewer robust writing-style effects. Our findings suggest that accessible NLP systems should adapt explanatory support to reader needs and text difficulty, rather than offering a single ``best'' summary style."
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<abstract>Language technologies increasingly mediate access to scientific information, yet their impact depends on whether they improve real reading experiences for diverse audiences. We study large language model (LLM)-generated scientific summaries as an accessibility intervention for neurodivergent adults, focusing on readers with ADHD and/or autism. Using a mixed-methods design, we first conducted a formative qualitative study with 10 neurodivergent adults to identify design needs. We then ran a quantitative experiment with 200 neurodivergent adults comparing five summary styles across lower- and higher-complexity scientific texts, measuring comprehension, trust, engagement, and cognitive load. Results show that writing-style effects depend on text complexity: structured LLM-generated summaries improved comprehension for higher-complexity articles but reduced comprehension for lower-complexity articles relative to a baseline human-generated extractive summary. Trust increased for simpler LLM summary styles, while engagement and cognitive load showed fewer robust writing-style effects. Our findings suggest that accessible NLP systems should adapt explanatory support to reader needs and text difficulty, rather than offering a single “best” summary style.</abstract>
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%0 Conference Proceedings
%T Measuring the Impact of LLM-Generated Scientific Summaries for Neurodivergent Readers
%A Rahman, Abraham Ur
%A Koba, Skyler
%A Michal, Audrey
%A Wilson, Steven R.
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F rahman-etal-2026-measuring
%X Language technologies increasingly mediate access to scientific information, yet their impact depends on whether they improve real reading experiences for diverse audiences. We study large language model (LLM)-generated scientific summaries as an accessibility intervention for neurodivergent adults, focusing on readers with ADHD and/or autism. Using a mixed-methods design, we first conducted a formative qualitative study with 10 neurodivergent adults to identify design needs. We then ran a quantitative experiment with 200 neurodivergent adults comparing five summary styles across lower- and higher-complexity scientific texts, measuring comprehension, trust, engagement, and cognitive load. Results show that writing-style effects depend on text complexity: structured LLM-generated summaries improved comprehension for higher-complexity articles but reduced comprehension for lower-complexity articles relative to a baseline human-generated extractive summary. Trust increased for simpler LLM summary styles, while engagement and cognitive load showed fewer robust writing-style effects. Our findings suggest that accessible NLP systems should adapt explanatory support to reader needs and text difficulty, rather than offering a single “best” summary style.
%U https://aclanthology.org/2026.nlp4pi-1.26/
%P 298-311
Markdown (Informal)
[Measuring the Impact of LLM-Generated Scientific Summaries for Neurodivergent Readers](https://aclanthology.org/2026.nlp4pi-1.26/) (Rahman et al., NLP4PI 2026)
ACL