DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums

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.