ArticleNature2024
Artificial intelligence and illusions of understanding in scientific research.
Article in Nature, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 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
127 citing papers in PubMed.
- Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.Journal of clinical nursing · 2026Article
- Rethinking non-human primate models for emerging viral infections after COVID-19.Laboratory animal research · 2026Review
- Telling Students to Evaluate Does Not Make It Happen: Task Stage, Offloading Tendency, and Error Detection in AI-Assisted Student Writing.Behavioral sciences (Basel, Switzerland) · 2026Article
- The future of large-scale experiments and their challenges in the digital era.Nature human behaviour · 2026Review
- Context-Stratified Mendelian Randomization: Exploiting Regional Exposure Variation to Explore Causal Effect Heterogeneity and Nonlinearity.Statistics in medicine · 2026Article
- Illusions of Understanding in the Sciences.Computational brain & behavior · 2026Article
- Illusions of Understanding from Outsourcing Thinking to LLMs.Computational brain & behavior · 2026Article
- The future of AI-assisted science is both fast and slow.Nature human behaviour · 2026Article
- Propagation and preservation of AI-discovered problem-solving strategies in human culture.Nature communications · 2026Article
- AI-Driven Data Extraction and AGREE-Based Greenness Evaluation of Analytical Methods for Natural Products: Insights from Selected Studies.Analytical chemistry · 2026Article
- Towards a research renaissance: Empowering early career researchers through mentored AI use.PLoS biology · 2026Article
- Review
- Article
- Artificial intelligence in contact sports research: Promise and peril for predicting and preventing brain injury.Experimental physiology · 2026Article
- The cognitive biases that may exacerbate inflationary and deflationary positions about large language models.Current opinion in behavioral sciences · 2026Review
- Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences.Ecology and evolution · 2026Article
- Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings.Behavior research methods · 2026Article
- Observational
- A reporting checklist for large language models in behavioural science.Nature human behaviour · 2026Article
- Research Ethics Committees (RECs) perspectives on large language models and AI ethics review: a South African case.BMC medical ethics · 2026Article
67 more citing papers are in PubMed but not listed here.
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
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
Indexed as
Identifiers
38448693What OpenQuestion holds
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.