Skip to content
#generative ai Open access

AI and your brain: every study on AI use and thinking

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Education

Abstract

Full interactive study, all charts and the latest version: https://iqmetrics.org/research/ai-and-your-brain/ The evidence is split, and it depends on how the AI is used. Of 56 studies published from 2023 to October 2026 that tested what generative AI does to people's own thinking, 19 found worse results, 16 better, 17 mixed and 4 no clear difference. Work done with AI usually improves, but when the AI is taken away the 25 experiments split: 8 found people did worse on their own, such as 17% lower exam scores after plain GPT-4, and 10 found they did better, mostly with tutors built to guide rather than answer. Surveys link heavy use to weaker critical thinking but cannot show cause, and brain studies are small. Every study we could find that tested what using ChatGPT and other AI tools does to people's own thinking: 56 so far, from brain scans to school trials, each checked against the paper, with what it found and what it cannot show. Key findings: The evidence is split. Of 56 studies from 2023 to October 2026, 19 found worse thinking, learning or memory with generative AI, 16 better, 17 mixed and 4 no clear difference. Once the AI is gone, results depend on the design. Of 25 experiments that tested people later without AI, 8 found they did worse, such as 17% lower exam scores after plain GPT-4, and 10 found they did better, often with tutors that guide instead of answering. Doing the work for you is the risk. People who let AI hand them answers learned less in several trials, such as quiz scores 17% lower after learning a coding library with AI and 57.5% vs 68.5% correct on a test 45 days later. Surveys point the same way but cannot show cause. 7 of 12 surveys linked heavier AI use to weaker critical thinking, memory or grades, including the Microsoft and Carnegie Mellon survey of 319 knowledge workers. Brain studies are small and early. The 11 EEG and fNIRS studies had 24 to 95 participants; the best known, MIT's 54-person preprint, found the weakest brain connectivity in ChatGPT users. One widely cited review was withdrawn. A 2025 meta-analysis of 51 studies that reported large learning gains from ChatGPT was retracted on 22 April 2026 over discrepancies in its analysis. Files: the report (PDF), the data (ai-and-your-brain-studies-2026-iqmetrics.csv) and a README with the column list. Method: https://iqmetrics.org/research/methods/ Licence: CC BY 4.0. Credit "IQ Metrics Research, AI and your brain 2026" with a link to https://iqmetrics.org/research/ai-and-your-brain/

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.