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A new scientific study warns that using artificial intelligence can erode our capacity for critical thinking. The research, carried out by a Microsoft and Carnegie Mellon University scientific team, found that the dependence on AI tools without questioning their validity reduces the cognitive effort applied to the work. In other words: AI can make us dumber if we use it wrong. AI can synthesize ideas, enhance reasoning, and encourage critical engagement, pushing us to see beyond the obvious and challenge our assumptions, Lev Tankelevitch, a senior researcher at Microsoft Research and coauthor of the study, tells me in an email interview. But to reap those benefits, Tankelevitch says users need to treat AI as a thought partner, not just a tool for finding information faster. Much of this comes down to designing a user experience that encourages critical thinking rather than passive reliance. By making AIs reasoning processes more transparent and prompting users to verify and refine AI-generated content, a well-designed AI interface can act as a thought partner rather than a substitute for human judgment. From ‘task execution’ to ‘task stewardship’ The researchwhich surveyed 319 professionalsfound that high confidence in AI tools often reduces the cognitive effort people apply to their work. Higher confidence in AI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking, the study states. This over-reliance stems from a mental model that assumes AI is competent in simple tasks. As one participant admitted in the study, its a simple task and I knew ChatGPT could do it without difficulty, so I just never thought about it. Critical thinking didnt feel relevant because, well, who cares. This mindset has major implications for the future of work. Tankelevitch tells me that AI is shifting knowledge workers from task execution to task stewardship. Instead of manually performing tasks, professionals now oversee AI-generated content, making decisions about its accuracy and integration. They must actively oversee, guide, and refine AI-generated work rather than simply accepting the first output, Tankelevitch says. The study highlights that when knowledge workers actively evaluate AI-generated outputs rather than passively accepting them, they can improve their decision-making processes. Research also shows that experts who effectively apply their knowledge when working with AI see a boost in output, Tankelevitch points out. AI works best when it complements human expertisedriving better decisions and stronger outcomes. The study found that many knowledge workers struggle to critically engage with AI-generated outputs because they lack the necessary domain knowledge to assess their accuracy. Even if users recognize that AI might be wrong, they dont always have the expertise to correct it, Tankelevitch explains. This problem is particularly acute in technical fields where AI-generated code, data analysis, or financial reports require deep subject matter knowledge to verify. The cognitive offloading paradox Confidence in AI can lead to a problem called cognitive offloading. This phenomenon isn’t new. Humans have long outsourced mental tasks to tools, from calculators to GPS devices. Cognitive offloading is not inherently negative. When done correctly, it allows users to focus on higher-order thinking rather than mundane, repetitive tasks, Tankelevitch points out. But the very nature of generative AIwhich produces complex text, code, and analysisbrings a new level of potential mistakes and problems. Many people might blindly accept AI outputs without questioning them (and quite often these outputs are bad or just plain wrong). This is especially the case when people feel the task is not important. Our study suggests that when people view a task as low-stakes, they may not review outputs as critically, Tankelevitch points out. The role of UX AI developers should keep that idea in mind when designing AI user experiences. These chat UX should be organized in a way that encourages verification, prompting users to think through the reasoning behind AI-generated content. Redesigning AI interfaces to aid in this new task stewardship process and encourage critical engagement is key to mitigating the risks of cognitive offloading. Deep reasoning models are already supporting this by making AIs processes more transparentmaking it easier for users to review, question, and learn from the insights they generate, he says. Transparency matters. Users need to understand not just what the AI says, but why it says it. You probably have seen this in an AI platform like Perplexity. Its interface offers a clear logical path that outlines the thoughts and actions that the AI takes to obtain a result. By redesigning AI interfaces to also include contextual explanations, confidence ratings, or alternative perspectives when needed, AI tools can shift users away from blind trust and towards active evaluation of the results. Another UX intervention may involve actively prompting the user for key aspects of the AI-generated output, prompting users to directly question and refine these outputs rather than passively accepting them.The final product of this open collaboration between AI and human is better, just like creative processes are often much better when two people work together as a team, especially when the strengths of one person complements the strengths of the other. Some will get dumber The study raises crucial questions about the long-term impact of AI on human cognition. If knowledge workers become passive consumers of AI-generated content, their critical thinking skills could atrophy. However, if AI is designed and used as an interactive, thought-provoking tool, it could enhance human intelligence rather than degrade it. Tankelevitch points out that this is not just theory. Its been proven on the field. For example, there are studies that show that AI can boost learning when used in the right way, he says. In Nigeria, an early study suggests that AI tutors could help students achieve two years of learning progress in just six weeks, he says. Another study showed that students working with tutors supported by AI were more likely to master key topics. The key, Tankelevitch tells me, is that this was all teacher-led: Educators guided the prompts and provided context, thus encouraging that vital critical thinking. AI has also demonstrated that it can enhance problem-solving in scientific research, where experts use it to explore complex hypotheses. Researchers using AI to assist in discovery still rely on human intuition and critical judgment to validate results, Tankelevitch notes. The most successful AI applications are those where human oversight remains central. Given the current state of generative AI, the technologys effect on human intelligence will not depend on the AI itself, but on how we choose to use it. UX designers can certainly help promote good behavior, but its up to us to do the right thing. AI can either amplify or erode critcal thinking, depending on whether we critically engage with its outputs or blindly trust them. The future of AI-assisted work will be determined not by the sophistication of the technology but by humans. My bet, as with every other technological revolution in the history of civilization, some people will get a lot dumber and others will get a lot smarter.
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The generative AI revolution shows no sign of slowing as OpenAI recently rolled out its GPT-4.5 model to paying ChatGPT users, while competitors have announced plans to introduce their own latest modelsincluding Anthropic, which unveiled Claude 3.7 Sonnet, its latest language model, late last month. But the ease of use of these AI models is having a material impact on the information we encounter daily, according to a new study published in Cornell Universitys preprint server arXiv. An analysis of more than 300 million documents, including consumer complaints, corporate press releases, job postings, and messages for the media published by the United Nations suggests that the web is being swamped with AI-generated slop. The study tracks the purported involvement of generative AI tools to create content across those key sectors, above, between January 2022 and September 2024. We wanted to quantify how many people are using these tools, says Yaohui Zhang, one of the study’s coauthors, and a researcher at Stanford University. The answer was, a lot. Following the November 30, 2022, release of ChatGPT, the estimated proportion of content in each domain that saw suggestions of AI generation or involvement skyrocketed. From a baseline of around 1.5% in the 11 months prior to the release of ChatGPT, the proportion of customer complaints that exhibited some sort of AI help increased tenfold. Similarly, the share of press releases that had hints of AI involvement rapidly increased in the months after ChatGPT became widely available. Which areas of the United States were more likely to adopt AI to help write complaints was made possible by the data accompanying the text of each complaint made to the Consumer Financial Protection Bureau (CFPB), the government agency that Donald Trump has now dissolved. In the 2024 data analyzed by the academics, complainants in Arkansas, Missouri, and North Dakota were the most likely to use AI, with its presence in around one in four complaints; while West Virginia, Idaho, and Vermont residents were least likelywhere between one in 20 and one in 40 showed AI evidence. Unlike off-the-shelf AI detection tools, Zhang and his colleagues developed their own statistical framework to determine whether something was likely AI-generated that compared linguistic patternsincluding word frequency distributionsin texts written before the release of ChatGPT against those known to have been generated or modified by large language models. The outputs were then tested against known human- or AI-written texts, with prediction errors lower than 3.3%, suggesting it was able to accurately discern one from the other. Like many, the team behind the work is worried about the impact of samizdat content flooding the webparticularly in so many areas, from consumer complaints to corporate and non-governmental organization press releases. I think [generative AI] is somehow constraining the creativity of humans, says Zhang.
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In business and sports, team dynamics impact outcomes. Whether youre pursuing profits or championships, knowing what triggers your teammates can help you avoid conflict and stay on task. The problem is that taking time to better understand each other isnt always our default setting, say John Eliot and Jim Guinn, authors of How To Get Along with Anyone: The Playbook for Predicting and Preventing Conflict at Work and at Home. Blowups appear to be substance-driven, but theyre actually people-driven, says Eliot. The first step toward preventing and resolving conflict is to focus on figuring out the people participating in it not the underlying problem. To work well with others, Guinn and Eliot recommend learning your teammates conflict triggers and go-to method of resolution. Three Conflict Triggers There are three distinct types of conflict, according to Eliot and Guinn. Not all conflicts trigger all people, and its common to have a blind spot for your own. Task conflict centers on getting things done. The person who is triggered by this type is goal- and deadline-driven. Their attitude is one where the ends justify the means. Process conflict centers on the way things get done. Someone who is bothered by process conflict doesnt focus on end goals or delivery dates. They care about methods, systems, or policies with a my way or the highway attitude. Relational conflict involves the people in disagreement and their habits, preferences, or tastes. In this case, the parties will fight over anything simply because they dont like one another. If there is no objective reason for a dispute, its likely relational. You can determine if a conflict type has triggered someone by watching how quickly they bring up the problem and if their tone changes. Knowing someones hot buttons can help you prevent a lot of conflict, says Eliot. You know what situation you should or should not go to with this person. Five Conflict Personalities How we handle conflict also follows patterns. When riled by one another, Eliot and Guinn say humans instinctively avoid, compete, analyze, collaborate, or accommodate, forming five go-to conflict personalities. The avoider sits back and waits to see if a conflict escalates before getting involved. This style lends itself to work efficiency and would rather get a job done themselves than delegate it. However, an avoider will also let a conflict fester or grow before resolving it. The competitor personality likes action and results. They prefer doing, and they thrive on clearly defined protocols. The downside is that a competitor can become impatient, rushing work, and they are often seen as being hard-nosed and inflexible. The analyzer has a penchant for evidence-based decision-making. They are patient and good at listening and gathering information. The weakness of an analyzer is that they can struggle with tight deadlines. They can also appear as controlling. The collaborator is a great communicator and has strong empathy for others. They make a good partner; however, they can lack time-management skills and are more prone to burnout. Finally, the accommodator is great at teamwork. In sports, they are the quarterback, often charismatic with an ability to account for different team members needs. Their weakness is that theyre often ego-driven, trying to do everything themselves. Conflict personality types and triggers work hand in hand. For example, someone who has a competitive conflict-handling approach will often be triggered by process. You don’t want to [resolve the conflict] with a lot of small talk, says Guinn. Instead, use what’s called the ‘domino technique.’ Knock the biggest issue first, which will knock over all of the minor issues. If someone has an analyzer personality style, they can be triggered by task conflicts, especially if they feel they are being rushed. Resolve this type of conflict with what Guinn calls a “momentum process.” Identify and knock out the most inconsequential issues, one punch at a time, he says. Then move onto relevant issues that represent low-hanging fruit. By understanding triggers and go-to styles for addressing them, you can get on the front side of conflict, predicting and preventing problems, says Guinn. In order to have good teams, you don’t need to have a complex understanding of psychology, he says. Just take a couple of simple steps in terms of listening and understanding. Know what tone of voice to use and what pace to move with this person. These are simple things everybody can do. Little steps go a long way in relationship- and team-building.
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