# Choosing Between an LLM versus Search for Learning: A HigherEd Student Perspective

arXiv: https://arxiv.org/abs/2409.13051 · HTML: https://mlsanity.com/paper/2409.13051/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [Inducing Sustained Creativity and Diversity in Large Language Models](https://mlsanity.com/paper/2603.19519/) (2026)
- [Are LLMs (Really) Ideological? An IRT-based Analysis and Alignment Tool for Perceived Socio-Economic Bias in LLMs](https://mlsanity.com/paper/2503.13149/) (2025)
- [Rethinking Search: A Study of University Students' Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving](https://mlsanity.com/paper/2510.17726/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are rapidly changing learning processes, as they are readily available to students and quickly complete or augment several learning-related activities with non-trivial performance. Such major shifts in learning dynamic have previously occurred when search engines and Wikipedia were introduced, and they augmented or traditional information consumption sources such as libraries and books for university students. We investigate the possibility of the next shift: the use of LLMs to find and digest information in the context of learning and how they relate to existing technologies such as the search engine. We conducted a study where students were asked to learn new topics using a search engine and an LLM in a within-subjects counterbalanced design. We used that study as a contextual grounding for a post-experience follow-up interview where we elicited student reflections, preferences, pain points, and general outlook of an LLM (ChatGPT) over a search engine (Google).