Search in Transition: A Study of University Students Perspectives on Using LLMs and Traditional Search Engines in English Test Problem Solving for Higher Study

Abstract: With the growing integration of AI in educational contexts, university students preparing for English language tests increasingly alternate between traditional search engines, such as Google, and LLMs to support their test-related problem-solving. This study examines students perceptions of these tools, focusing on usability, efficiency, and their integration into English language test preparation workflows.Using a mixed-methods approach, we surveyed 140 university students from diverse academic disciplines and conducted in-depth interviews with 20 participants. Quantitative analyses, including ANOVA and chi-square tests, were employed to evaluate differences in perceived efficiency, satisfaction, and overall tool preference. The qualitative findings indicate that students frequently switch between GPT and Google depending on task demands, relying on Google for credible, multi-source information and rule verification, while using GPT for summarization, explanation, paraphrasing, and drafting responses for English test tasks. As neither tool alone was found to adequately support all aspects of English language test problem solving, participants expressed a strong preference for a hybrid solution. In response, we propose a prototype in the form of a chatbot embedded within a search interface, combining GPTs conversational strengths with Google reliability to improve English language test preparation and reduce cognitive load.