DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums
arXiv:2606.27619v1 Announce Type: cross Abstract: Dyslexic learners increasingly use artificial intelligence (AI) tools to support reading, writing, organisation, and study-related tasks. However, their lived experiences with these tools remain largely underexamined. This paper proposes DysLexLens, a low-resource LLM framework, designed to analyse dyslexic learners experience with AI through online forum discussions. DysLexLens is designed as an end-to-end, evidence-traceable architecture which transforms noisy social media posts into a dictionary-driven corpora, provides knowledge-graph (KG)-
The proliferation of AI tools provides both opportunities and challenges for specialized user groups like dyslexic learners, necessitating specific frameworks to understand their engagement.
Understanding how AI tools are adopted and perceived by niche user communities offers critical insights into user experience design, accessibility, and the broader societal impact of large language models.
This framework offers a structured way to gather and analyse data on specific user experiences with AI, moving beyond general usage patterns to focus on functional and cognitive needs.
- · AI accessibility tool developers
- · Dyslexic learners
- · Educational technology providers
- · Researchers in human-computer interaction
- · AI tools lacking adaptive features
- · Generic AI user experience methodologies
DysLexLens provides a method for extracting actionable insights from online discussions about AI tool usage by dyslexic individuals.
This could lead to the development of more tailored and effective AI applications and interfaces specifically designed for neurodiverse populations.
Improved accessibility and utility of AI for dyslexic learners could significantly enhance their educational and professional outcomes, fostering greater inclusion in digital workforces.
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Read at arXiv cs.LG