A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users
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.
Cite
@article{arxiv.2603.17592,
title = {A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users},
author = {Christos Koutsiaris},
journal= {arXiv preprint arXiv:2603.17592},
year = {2026}
}
Comments
9 pages, 5 figures, 2 tables; MSc dissertation reformatted as conference paper; extended version available at github.com/unseen1980/acro-helper