Title: Rethinking Search: A Study of University Students' Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving Show Chinese title

Title: 重新思考搜索：大学生在学术问题解决中使用大语言模型和传统搜索引擎的视角研究

Abstract: With the increasing integration of Artificial Intelligence (AI) in academic problem solving, university students frequently alternate between traditional search engines like Google and large language models (LLMs) for information retrieval. This study explores students' perceptions of both tools, emphasizing usability, efficiency, and their integration into academic workflows. Employing a mixed-methods approach, we surveyed 109 students from diverse disciplines and conducted in-depth interviews with 12 participants. Quantitative analyses, including ANOVA and chi-square tests, were used to assess differences in efficiency, satisfaction, and tool preference. Qualitative insights revealed that students commonly switch between GPT and Google: using Google for credible, multi-source information and GPT for summarization, explanation, and drafting. While neither tool proved sufficient on its own, there was a strong demand for a hybrid solution. In response, we developed a prototype, a chatbot embedded within the search interface, that combines GPT's conversational capabilities with Google's reliability to enhance academic research and reduce cognitive load. Show Chinese abstract

Abstract: 随着人工智能（AI）在学术问题解决中的日益融合，大学生经常在传统的搜索引擎如谷歌和大型语言模型（LLMs）之间切换以进行信息检索。 本研究探讨了学生对这两种工具的看法，重点强调可用性、效率以及它们在学术工作流程中的整合。 采用混合方法，我们调查了来自不同学科的109名学生，并对12名参与者进行了深入访谈。 定量分析，包括方差分析和卡方检验，用于评估效率、满意度和工具偏好的差异。 定性见解显示，学生经常在GPT和谷歌之间切换：使用谷歌获取可信的多来源信息，而使用GPT进行总结、解释和起草。 尽管这两种工具单独使用都不足以满足需求，但对一种混合解决方案有强烈的需求。 作为回应，我们开发了一个原型，一个嵌入搜索界面中的聊天机器人，结合GPT的对话能力与谷歌的可靠性，以增强学术研究并减少认知负担。