ArticlePNAS nexus2025
Experimental evidence of the effects of large language models versus web search on depth of learning.
Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Why using AI as a learning coach can improve education.Nature human behaviour · 2026Article
- Illusions of Understanding from Outsourcing Thinking to LLMs.Computational brain & behavior · 2026Article
- Social dynamics of AI adoption in parents' educational decisions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Advertising and large language models: a new frontier influencing medical practice.Eye (London, England) · 2026Article
- Reconciling how clinical reasoning is learned in the age of artificial intelligence.NPJ digital medicine · 2026Article
- Gaps in large language model awareness, usage, and perceptions in the United States: Evidence from a nationally representative longitudinal survey.PNAS nexus · 2026Article
- Flight rules for clinical AI: lessons from aviation for human-AI collaboration in medicine.NPJ digital medicine · 2026Review
- Recalibrating academic expertise in the age of generative AI.Patterns (New York, N.Y.) · 2026Review
- Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions.Frontiers in psychology · 2026Article
- The causal effects of artificial intelligence use on metacognition, engagement, and knowledge transfer in educational contexts.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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 in forming their advice, and, more importantly, create advice that is sparser, less original, and ultimately less likely to be adopted by recipients. Results from seven online and laboratory experiments (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.