Development and Testing of Generative AI-Based Browser Extension for Personalized Independent Learning

Abstract

The rapid growth of generative artificial intelligence (Generative AI) has opened new possibilities for supporting self-directed learning in higher education. Despite this, many students still struggle to grasp academic terminology when studying independently, especially while using online learning resources. This Research and Development (R&D) study aims to develop and test a browser extension that integrates an open-source generative AI model, specifically a large language model (LLM), to assist students in learning academic terminology. The extension was developed following the Software Development Life Cycle (SDLC) framework. Built with the WXT (Next-Gen Web Extension Framework), the system connects to a LLM model via an API and incorporates prompt engineering as well as option-based personalized learning preferences. To verify its functionality and feasibility, both white-box and black-box testing were performed. The results show that the browser extension is feasible to use and can provide personalized explanations, potentially enhancing students’ comprehension of academic terminology during independent study. In conclusion, integrating a free, open-source generative AI model into a browser extension developed with WXT, incorporating prompt engineering and option-based personalized learning preferences, it demonstrates the potential to serve as a tool that supports and engages students in self-directed learning, particularly in understanding academic terminology.