Rethinking Search: A Study of University Students' Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 5 of its 13 references that are in the map. First posted on arXiv 20 Oct 2025.
Builds on
5 of 13 references.
References · 5
- 2023Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experimentcited by 36
- 2024Large Language Models vs. Search Engines: Evaluating User Preferences Across Varied Information Retrieval Scenarioscited by 14
- 2023ChatGPT vs. Google: A Comparative Study of Search Performance and User Experiencecited by 12
- 2024Choosing Between an LLM versus Search for Learning: A HigherEd Student Perspectivecited by 3
- 2024Understanding Help-Seeking Behavior of Students Using LLMs vs. Web Search for Writing SQL Queriescited by 2
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- 2024Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks
- 2026From Tool to Teacher: Rethinking Search Systems as Instructive Interfaces
- 2026Large Language Models in Teaching and Learning: Reflections on Implementing an AI Chatbot in Higher Education ↗
- 2025From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education
- 2024Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines
- 2024From Questions to Insightful Answers: Building an Informed Chatbot for University Resources
- 2024The Effects of Generative AI on Computing Students' Help-Seeking Preferences
- 2025Advancing Academic Chatbots: Evaluation of Non Traditional Outputs