过去几年，人工智能在一般信息收集方面的应用出现了大规模爆炸式增长，这已经不是什么秘密了。最近的趋势,虽然,是像ChatGPT, Claude,和Gemini这样的大型语言模型(LLMs)越来越多地被用于验证和消费新闻;皮尤研究中心去年的报告发现，五分之一的美国青少年经常使用LLM来获取新闻,，而四分之一的年轻人报告至少使用一次它们用于此目的。
It的 no secret that the last few years have seen a massive explosion in the use of artificial intelligence for general information-gathering. An even more recent trend, though, is how large language models (LLMs) like ChatGPT, Claude, and Gemini are increasingly being used for verifying and consuming news; reports from the Pew Research Center over the last year found that one-in-five U.S. teens regularly use LLMs to get their news, while one-in-four young adults have reported using them for that purpose at least once.
麻省理工学院媒体实验室的一项新的开放获取研究应该会让其中一些用户暂停: 研究人员发现，, 在一个月内, 依靠人工智能系统验证事实的参与者，当他们的聊天机器人被拿走时，他们自己检测错误信息的能力实际上变得更差。
A new open-access study from the MIT Media Lab should give some of those users pause: Researchers found that, over the course of a month, participants who relied on AI systems to verify facts actually got worse at detecting misinformation on their own when their chatbots were taken away.
这种现象,通常被称为“AI依赖性悖论,”，已在广泛的知识领域中观察到,，例如2025年的研究发现，使用人工智能的医生自行检测癌症的能力较差。这种动态反映了围绕所谓的 “deskilling” ( 或 “cognitive offloading”) 的更广泛的技术趋势，这些趋势在几十年来已有充分记录,，从削弱我们数学技能的计算器到影响我们自然方向感的全球定位系统 (GPS) 技术。
This phenomenon, which is often referred to as the “AI dependency paradox,” has been observed in a wide range of knowledge domains, like the 2025 study that found that doctors who used AI got worse at detecting cancer on their own. The dynamic mirrors broader tech trends around so-called “deskilling” (or “cognitive offloading”) that have been well-documented for decades, from calculators weakening our math skills to Global Positioning System (GPS) technologies impacting our natural sense of direction.
然而, 研究表明，当人工智能不再存在: 时，出现了新的问题: 到第四周, 参与者 在新新闻项目上的独立表现与研究开始前相比下降了 15 个百分点。 (大约四分之一的参与者实际上表示感觉自己的检测能力有所提高,，尽管他们的表现有所下降。)
However, the study showed that a new wrinkle emerged when the AI was no longer present: By week four, participants unassisted performance on new news items declined by 15 percentage points compared to before the study started. (Roughly a quarter of all participants actually reported feeling that they were getting better at detection, even as their performance declined.)
“用户对这些 ‘magical LLM, 感到兴奋，但忘记了它们’只是预测序列中下一个 ‘token的统计模型 [of字母/words],” 麻省理工学院说媒体艺术与科学 (MAS) 博士生 Anku Rani, 与 MAS 博士生 Valdemar Danry 共同撰写了一篇关于该研究, 的新论文。 “通过扩展此,会出现许多令人印象深刻的行为，但它具有真正的局限性,，无论是模型可以可靠地生成什么，还是对使用它的人产生更广泛的影响。”
“Users get excited about these ‘magical LLMs, but forget that they’re just statistical models that predict the next ‘token in a sequence [of letters/words],” says MIT media arts and sciences (MAS) PhD student Anku Rani, co-lead author of a new paper about the research, alongside fellow MAS PhD student Valdemar Danry. “Many impressive behaviors emerge from scaling this, but it comes with real limitations, both in what the model can reliably generate and in its broader impact on the people using it.”
定性分析确定了不同的行为模式,，团队将所有参与者的五分之一标记为"依赖开发者”，他们逐渐从主动自力更生转变为被动接受人工智能指导。
Qualitative analysis identified distinct behavioral patterns, with the team labeling one-fifth of all participants as "Dependency Developers” who gradually shifted from active self-reliance to passive acceptance of AI guidance.
在实验后调查, 中，一位受访者明确承认这一转变, 并指出他们在此过程中的被动角色。 “虽然[聊天机器人]确实强调你必须检查多个来源以确保故事是真实的,他们没有’教我很多关于探索图像本身的背景,”参与者说。
In the post-experiment survey, one respondent explicitly acknowledged this transition, noting their passive role in the process. “While [the chatbots] did emphasize that you must check across multiple sources to make sure a story is true, they didn’t teach me much about exploring the context of the images themselves,” the participant said.
研究小组表示，这些人工智能模型在处理情绪激动的突发新闻,时特别容易出错，特朗普总统’最近的暗杀企图和伊朗战争期间的重大事件所伴随的广泛错误信息就证明了这一点。 (作者还指出，的用来训练人工智能模型的原始人工新闻内容越来越不可靠，并且%2有偏见,进一步加剧了问题。)
The research team said that these AI models are particularly vulnerable to mistakes in the midst of emotionally charged breaking news, as exhibited by the widespread misinformation that accompanied President Trump的 recent assassination attempt and major events during the Iranian war. (The authors also point out that the original human-created news content that的 used to train the AI models is increasingly unreliable and/or biased, further exacerbating the problem.)
Danry 和 Rani 在 2026 年 CHI 计算系统人为因素会议, 上发表的论文, 是由助理教授 Paul Pu Liang, 高级研究科学家 Andrew Lippman, 和高级作者 Germeshausen 媒体艺术与科学教授 Pattie Maes, 共同撰写的。
The paper, which Danry and Rani presented at the 2026 CHI Conference on Human Factors in Computing Systems, was co-authored by Assistant Professor Paul Pu Liang, Senior Research Scientist Andrew Lippman, and senior author Pattie Maes, the Germeshausen Professor of Media Arts and Sciences.
解决方案: 成为教练, 不是拐杖
The solution: Being a coach, not a crutch
研究人员表示，他们的项目结果表明，人工智能与用户交互的具体方式决定了其影响是�%9作为教练,还是作为拐杖。” 研究发现，仅在当下提供帮助的对话策略与真正支持主动学习和技能发展的对话策略之间存在明显区别。
The researchers say that the results of their project suggest that the specific way in which an AI interacts with a user determines whether its impact will be “as a coach, versus as a crutch.” The study found a clear distinction between conversational strategies that simply help in the moment and those that actually support active learning and skill development.
对于后者,，媒体实验室团队在, 上发现了几种与更强的独立检测相关的策略，即使这些策略最初会降低交互过程中的性能。这包括人工智能提出引导性问题,的苏格拉底式方法以及所谓的“深度探测,”，其中如果用户似乎偏离了正确的答案，系统会提供温和的有说服力的陈述。
For the latter, the Media Lab team uncovered several strategies associated with stronger independent detection later on, even if the strategies initially slowed down performance during the interaction. This included the Socratic method of the AI asking guided questions, as well as so-called “deep probing,” where the system provides gently persuasive statements if the user appears to be veering away from the correct response.
“AI ‘通过提供直接答案告诉’更有可能培养信任,，而那些‘通过苏格拉底式提问询问’的人更能吸引人们真正学习如何自己辨别真相,” 说。 “但是’很大程度上是速度和努力之间的权衡。”
“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的 very much a trade-off between speed and effort.”
Rani 指出了这项为期一个月的研究, 的一些关键局限性，从包含大约 50 条经过验证的新闻项目的小数据集到对美国和英国的人口统计重点。她表示，未来,，该团队希望对更多地理位置不同的群体,（包括资源匮乏的社区,）进行类似的实验，并且还渴望探索其他多模式交互策略—（例如与文化适应性数字双胞胎交互而不是基于文本的聊天机器人—）是否有助于人们提高检测错误信息的能力。
Rani noted a few key limitations to the one-month study, from the small dataset of roughly 50 validated news items to the demographic focus on the United States and the United Kingdom. In the future, she says that the team hopes to do similar experiments with more geographically diverse cohorts, including low-resource communities, and is also eager to explore whether other multi-modal interaction strategies — like interacting with culturally adaptive digital twins instead of text-based chatbots — help people improve their abilities to detect misinformation.
在更高的层面,，研究人员希望教育工作者在制定将人工智能工具纳入学校课程的教学计划时可以检查该项目。
At a higher level, the researchers hope that the project will be something that educators can examine as they develop teaching plans that incorporate AI tools into their school curricula.
“It的 对于提高我们的学校和学术界对使用人工智能作为学习工具的缺点的认识尤其重要,” Maes 说。 “人们需要知道，如果他们‘委派’他们的思考,，他们’就不会在解决特定问题方面做得更好。最终, 质疑和分析信息的能力对每个人都很重要,，因为它使我们能够解决问题并形成我们自己对世界的独立意见。”
“It的 especially important to raise awareness in our schools and academic communities about the shortcomings of using AI as learning tools,” says Maes. “People need to know that if they ‘delegate their thinking, they’re not going to get better at that particular brand of problem-solving. Ultimately, the ability to question and analyze information is important for everyone, because it empowers us to solve problems and form our own independent opinions about the world.”
Danry 补充道，快速发展的机器学习和深度学习领域需要持续教育法学硕士的优点和缺点。
Danry adds that the rapidly-evolving field of machine learning and deep learning will require continuous education on the benefits and drawbacks of LLMs.
“还有’需要做很多工作来确保我们不’完全卸载我们希望能够继续对这些模型执行的关键任务,”他说。 “我们需要培养一种新的人工智能素养。”
“There的 a lot of work to do in making sure that we don’t just fully offload critical tasks that we want to be able to keep on doing to these models,” he says. “We need to develop a new kind of AI literacy.”