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.

翻译：随着人工智能在学术问题解决中的日益普及，大学生在信息检索时频繁交替使用Google等传统搜索引擎与大型语言模型。本研究探讨了学生对这两种工具的认知，重点关注其可用性、效率及其在学术工作流中的整合。采用混合方法，我们对来自不同学科的109名学生进行了问卷调查，并对12名参与者进行了深度访谈。通过方差分析和卡方检验等定量方法评估了效率、满意度及工具偏好方面的差异。定性分析揭示，学生常在GPT与Google之间切换：使用Google获取可信的多源信息，而使用GPT进行总结、解释和草拟。尽管两种工具单独使用均显不足，但对混合解决方案存在强烈需求。为此，我们开发了一个原型系统——一个内嵌于搜索界面的聊天机器人，它结合了GPT的对话能力与Google的可靠性，旨在提升学术研究效率并减轻认知负荷。