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.

翻译：本文描述了一款浏览器扩展的设计、实现与评估，该扩展能为用户悬停在网页上的技术缩写和缩略词时提供上下文帮助。该扩展结合了精选技术词典与OpenAI的大语言模型，通过轻量级工具提示浮窗提供按需定义。其双层人工智能流水线（包括Google Cloud的自然语言处理分类API和OpenAI的ChatGPT）在激活工具提示逻辑前，先将每个访问页面分类为技术相关页面，从而减少误报检测。一项包含25名低至中等数字素养用户的混合方法研究评估了该工具对阅读理解和信息检索时间的影响。结果显示，92%的参与者报告对技术术语的理解有所提升，96%确认相比手动网页搜索节省了时间，且所有参与者认为工具提示无干扰性。基于词典的定义平均用时2135毫秒呈现，而AI生成定义平均耗时16429毫秒，每个缩写词的手动搜索平均用时17200毫秒。该工作展示了一种实用、实时的数字素养鸿沟弥合方法，并指出可将上下文帮助扩展至医学、法律和金融等其他领域。