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A Contextual Help Browser Extension to Assist Digital Illiterate Internet Users

Christos Koutsiaris

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.

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.
Paper Structure (30 sections, 5 figures, 2 tables)

This paper contains 30 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Digital Illiteracy
  4. Acronyms as Barriers to Comprehension
  5. Contextual Help Tools
  6. NLP for Web Content Classification
  7. Research Questions
  8. System Architecture
  9. Extension Building Blocks
  10. Four-Phase Processing Pipeline
  11. Dictionary Search Tab Page
  12. Methodology
  13. Research Design
  14. Participants
  15. Procedure
  16. ...and 15 more sections

Figures (5)

  • Figure 1: Four-phase pipeline of the Acro Helper browser extension. Dashed arrows indicate calls to external AI services.
  • Figure 2: Self-reported improvement in understanding technical terms after using the extension. 92% reported at least moderate improvement.
  • Figure 3: 96% of participants confirmed the extension saved time compared with searching a search engine manually.
  • Figure 4: All participants expressed willingness to recommend the extension; none were neutral or negative.
  • Figure 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.