Experimental evidence of the effects of large language models versus web search on depth of learning.

ABSTRACT: The effects of using large language models (LLMs) versus traditional web search on depth of learning are explored. A theory is proposed that when individuals learn about a topic from LLM syntheses, they risk developing shallower knowledge than when they learn through standard web search, even when the core facts in the results are the same. This shallower knowledge accrues from an inherent feature of LLMs-the presentation of results as summaries of vast arrays of information rather than individual search links-which inhibits users from actively discovering and synthesizing information sources themselves, as in traditional web search. Thus, when subsequently forming advice on the topic based on their search, those who learn from LLM syntheses (vs. traditional web links) feel less invested i

SUBMITTER: Melumad S 

PROVIDER: S-EPMC12560091 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature