DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums
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Computer Science > Artificial Intelligence
arXiv:2606.27619 (cs)
[Submitted on 26 Jun 2026]
Title:DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums
Authors:Dana Rezazadegan, Atie Kia, Phongpadid Nandavong, Dominique Carlon, Jeremy Nguyen, Abhik Banerjee, James Marshall, Anthony McCosker, Yong-Bin Kang
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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)-based question reasoning, generates verifiable query responses, and enables response evaluation through quantitative and human-grounded assessment. DysLexLens has four key features. First, it employs a dictionary-driven filtering method to construct a more focused Reddit corpus on dyslexia and AI, filtering out noisy and weakly related posts to improve the relevance of data collected from low-resource forum contexts. Second, it integrates LLM-assisted semantic analysis with KG-based query reasoning to uncover meaningful patterns. Third, it has quantitative evaluation metrics (RAGAS and Query Robustness) to measure LLM-generated response performance. Fourth, it provides structured qualitative validation guidelines for assessing response quality, with a specific focus on hallucination and evidence alignment. We demonstrate the effectiveness of DysLexLens using dyslexia-related Reddit forum data and 30 questions. The results show its potential generalisability to other low-resource forum data contexts. DysLexLens, sample data, questions and evaluation results are available at Github to support reproducibility.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.27619 [cs.AI] |
| (or arXiv:2606.27619v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27619
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Fahimeh Rezazadegan [view email][v1] Fri, 26 Jun 2026 00:32:32 UTC (3,518 KB)
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