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

2026-06-25

AI Summary

This study addresses the lack of systematic analysis on the real-world experiences of learners with dyslexia using AI tools in low-resource online forums. The authors propose a lightweight, end-to-end, and evidence-traceable large language model framework that effectively extracts user interaction insights from noisy forum data such as Reddit. The framework integrates dictionary-driven data filtering, knowledge graph–enhanced query reasoning, and a rigorous evaluation mechanism combining RAGAS and Query Robustness metrics with structured human validation. Experimental results on 30 questions and real-world Reddit data demonstrate the framework’s strong generalization capability and adaptability to low-resource settings. All code and materials are publicly released to facilitate reproducibility.

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.

Problem

• dyslexia
• AI tools
• online forums
• low-resource
• lived experiences

Innovation

• low-resource LLM
• knowledge graph reasoning
• dictionary-driven filtering
• hallucination mitigation
• evidence-traceable AI