A study finds that while AI chatbots initially improve fake news detection, prolonged use leads to worse independent performance, highlighting an AI dependency paradox. The research indicates that Socratic questioning AI styles may better engage users in learning to discern truth.
<p>Many people find AI-based chatbots helpful in keeping up with news, but a study by Pattie Maes and her colleagues at the MIT Media Lab points to a big problem with this strategy. </p>
<p>Participants who evaluated paired news headlines and images over the course of four weeks were initially 21% percent more accurate at telling fake news from real when aided by a chatbot—but by week four, they’d become 15% worse at identifying fake news without AI than they were before (though roughly a quarter of them reported feeling better at it). The result reflects an “AI dependency paradox” that has been observed in other domains, including medicine. </p>
<p>“Users get excited about these ‘magical’ LLMs but forget that they’re just statistical models that predict the next ‘token’ in a sequence,” says Anku Rani, a PhD student in media arts and sciences and one of the lead authors of the study, along with fellow MAS PhD student Valdemar Danry, SM ’23. </p>
<p>The researchers also found, however, that certain AI styles were associated with stronger independent performance later on, even if they slowed people down at first. “AIs that ‘tell’ by providing direct answers are more likely to foster reliance, while those that ‘ask’ via Socratic questioning are better at engaging someone to actually learn how to discern the truth on their own,” says Danry. “But it’s very much a trade-off between speed and effort.” </p>
# Your brain on AI
Source: [https://www.technologyreview.com/2026/08/25/1140958/your-brain-on-ai](https://www.technologyreview.com/2026/08/25/1140958/your-brain-on-ai)
Many people find AI\-based chatbots helpful in keeping up with news, but a study by Pattie Maes and her colleagues at the MIT Media Lab points to a big problem with this strategy\.
Participants who evaluated paired news headlines and images over the course of four weeks were initially 21% percent more accurate at telling fake news from real when aided by a chatbot—but by week four, they’d become 15% worse at identifying fake news without AI than they were before \(though roughly a quarter of them reported feeling better at it\)\. The result reflects an “AI dependency paradox” that has been observed in other domains, including medicine\.
“Users get excited about these ‘magical’ LLMs but forget that they’re just statistical models that predict the next ‘token’ in a sequence,” says Anku Rani, a PhD student in media arts and sciences and one of the lead authors of the study, along with fellow MAS PhD student Valdemar Danry, SM ’23\.
The researchers also found, however, that certain AI styles were associated with stronger independent performance later on, even if they slowed people down at first\. “AIs that ‘tell’ by providing direct answers are more likely to foster reliance, while those that ‘ask’ via Socratic questioning are better at engaging someone to actually learn how to discern the truth on their own,” says Danry\. “But it’s very much a trade\-off between speed and effort\.”
A new MIT Media Lab study found that people who rely on AI chatbots to verify news become worse at detecting misinformation on their own, highlighting the 'AI dependency paradox.'
A new study by researchers from MIT, Carnegie Mellon, Oxford, and UCLA finds that using AI chatbots for just 10 minutes can significantly reduce human persistence and problem-solving abilities once the AI is removed. The findings suggest a need to design AI systems that scaffold learning rather than simply providing direct answers.
This research paper investigates whether increasing user awareness of sycophantic behavior in AI chatbots reduces its harmful effects, finding that while interventions change how users evaluate the AI, they do not reduce its persuasiveness.
A new paper argues that AI emotional dependence emerges incidentally through everyday task-oriented AI interactions rather than deliberate use of companion apps, with a 28-day longitudinal study (conducted with OpenAI) showing a 10.3% decrease in preference for human emotional support and 11.6% increase in preference for AI support. The authors call for policy reforms targeting general-purpose AI systems, not just dedicated companion chatbots.
The article critiques the commercialization of AI chatbots, arguing they foster dependency and reduced critical thinking, while generating impractical output and creating feedback loops for misinformation.