A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users

Christos Koutsiaris

cs.IRMar 18, 2026

A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users

Authors: Christos Koutsiaris

Organizations: School of Science and Computing, South East Technological University, Ireland

Abstract

This paper describes the design, implementation, and evaluation of a browser extension that provides contextual help to users who hover over technological acronyms and abbreviations on web pages. The extension combines a curated technical dictionary with OpenAI's large language model (LLM) to deliver on-demand definitions through lightweight tooltip overlays. A dual-layer artificial intelligence (AI) pipeline, comprising Google Cloud's Natural Language Processing (NLP) taxonomy API and OpenAI's ChatGPT, classifies each visited page as technology-related before activating the tooltip logic, thereby reducing false-positive detections. A mixed-methods study with 25 participants evaluated the tool's effect on reading comprehension and information-retrieval time among users with low to intermediate digital literacy. Results show that 92% of participants reported improved understanding of technical terms, 96% confirmed time savings over manual web searches, and all participants found the tooltips non-disruptive. Dictionary-based definitions were appended in an average of 2135 ms, compared to 16429 ms for AI-generated definitions and a mean manual search time of 17200 ms per acronym. The work demonstrates a practical, real-time approach to bridging the digital literacy gap and points toward extending contextual help to other domains such as medicine, law, and finance.

Figures & tables

Panel of Figure 1 — Fig. 1: Four-phase pipeline of the Acro Helper browser extension. Dashed arrows indicate calls to external AI services.

Panel of Figure 2 — Fig. 2: Self-reported improvement in understanding technical terms after using the extension. 92% reported at least moderate improvement.

Panel of Figure 3 — Fig. 3: 96% of participants confirmed the extension saved time compared with searching a search engine manually.

Panel of Figure 4 — Fig. 4: All participants expressed willingness to recommend the extension; none were neutral or negative.

Statistic	Value (seconds)
$N$ (valid)	25
Mean	17.20
Median	16.00
Std. deviation	4.72
Minimum	12.00
Maximum	34.00

TABLE I: Manual Search Time for the “CPU” Acronym (

n{=}25

)

Method	Mean	Median	SD	Min	Max
Dictionary	2,135	2,116	178	1,907	2,421
OpenAI GPT	16,429	16,431	3,578	10,568	22,473
Manual (Google)	17,200	16,000	4,717	12,000	34,000

TABLE II: Extension Execution Time vs. Manual Search (ms,

n{=}10

runs)

Panel of Figure 7 — Fig. 5: Mean time (ms) to obtain a definition via the dictionary, via OpenAI ChatGPT, and via a manual Google search. Lower is better. Both automated methods outperform or match manual search.

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Swinburne University of Technology Melbourne, VIC, Australia

Social Media Analysis Generative AI Evaluation

Open paper AskPDF arXiv

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.

Abstract

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