Learning with AI falls short compared to old-fashioned web search

Shiri Melumad, University of Pennsylvania Since the release of ChatGPT in late 2022, millions of people have started using large language models to access knowledge. And it’s easy to understand their appeal: Ask a question, get a polished synthesis and move on – it feels like effortless learning. However, a new paper I co-authored offers experimental evidence that this ease may come at a cost: When people rely on large language models to summarize information on a topic for them, they tend to develop shallower knowledge about it compared to learning through a standard Google search. Co-author Jin Ho Yun and I, both professors of marketing, reported this finding in a paper based on seven studies with more than 10,000 participants. Most of the studies used the same basic paradigm: Participants were asked to learn about a topic – such as how to grow a vegetable garden – and were randomly assigned to do so by using either an LLM like ChatGPT or the “old-fashioned way,” by navigating links using a standard Google search. No restrictions were put on how they used the tools; they could search on Google as long as they wanted and could continue to prompt ChatGPT if they felt they wanted more information. Once they completed their research, they were then asked to write advice to a friend on the topic based on what they learned. The data revealed a consistent pattern: People who learned about a topic through an LLM versus web search felt that they learned less, invested less effort in subsequently writing their advice, and ultimately wrote advice that was shorter, less factual and more generic. In turn, when this advice was presented to an independent sample of readers, who were unaware of which tool had been used to learn about the topic, they found the advice to be less informative, less helpful, and they were less likely to adopt it. We found these differences to be robust across a variety of contexts. For example, one possible reason LLM users wrote briefer and more generic advice is simply that the LLM results exposed users to less eclectic information than the Google results. To control for this possibility, we conducted an experiment where participants were exposed to an identical set of facts in the results of their Google and ChatGPT searches. Likewise, in another experiment we held constant the search platform – Google – and varied whether participants learned from standard Google results or Google’s AI Overview feature. The findings confirmed that, even when holding the facts and platform constant, learning from synthesized LLM responses led to shallower knowledge compared to gathering, interpreting and synthesizing information for oneself via standard web links.

Why it matters

Why did the use of LLMs appear to diminish learning? One of the most fundamental principles of skill development is that people learn best when they are actively engaged with the material they are trying to learn. When we learn about a topic through Google search, we face much more “friction”: We must navigate different web links, read informational sources, and interpret and synthesize them ourselves. While more challenging, this friction leads to the development of a deeper, more original mental representation of the topic at hand. But with LLMs, this entire process is done on the user’s behalf, transforming learning from a more active to passive process.

What’s next?

To be clear, we do not believe the solution to these issues is to avoid using LLMs, especially given the undeniable benefits they offer in many contexts. Rather, our message is that people simply need to become smarter or more strategic users of LLMs – which starts by understanding the domains wherein LLMs are beneficial versus harmful to their goals. Need a quick, factual answer to a question? Feel free to use your favorite AI co-pilot. But if your aim is to develop deep and generalizable knowledge in an area, relying on LLM syntheses alone will be less helpful. As part of my research on the psychology of new technology and new media, I am also interested in whether it’s possible to make LLM learning a more active process. In another experiment we tested this by having participants engage with a specialized GPT model that offered real-time web links alongside its synthesized responses. There, however, we found that once participants received an LLM summary, they weren’t motivated to dig deeper into the original sources. The result was that the participants still developed shallower knowledge compared to those who used standard Google. Building on this, in my future research I plan to study generative AI tools that impose healthy frictions for learning tasks – specifically, examining which types of guardrails or speed bumps most successfully motivate users to actively learn more beyond easy, synthesized answers. Such tools would seem particularly critical in secondary education, where a major challenge for educators is how best to equip students to develop foundational reading, writing and math skills while also preparing for a real world where LLMs are likely to be an integral part of their daily lives. The Research Brief is a short take on interesting academic work. Shiri Melumad, Associate Professor of Marketing, University of Pennsylvania This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Yurou Wang, University of Alabama

Imagine a student using a writing assistant powered by a generative AI chatbot. As the bot serves up practical suggestions and encouragement, insights come more easily, drafts polish up quickly and feedback loops feel immediate. It can be energizing. But when that AI support is removed, some students report feeling less confident or less willing to engage.

These outcomes raise the question: Can AI tools genuinely boost student motivation? And what conditions can make or break that boost?

As AI tools become more common in classroom settings, the answers to these questions matter a lot. While tools for general use such as ChatPGT or Claude remain popular, more and more students are encountering AI tools that are purpose-built to support learning, such as Khan Academy’s Khanmigo, which personalizes lessons. Others, such as ALEKS, provide adaptive feedback. Both tools adjust to a learner’s level and highlight progress over time, which helps students feel capable and see improvement. But there are still many unknowns about the long-term effects of these tools on learners’ progress, an issue I continue to study as an educational psychologist.

What the evidence shows so far

Recent studies indicate that AI can boost motivation, at least for certain groups, when deployed under the right conditions. A 2025 experiment with university students showed that when AI tools delivered a high-quality performance and allowed meaningful interaction, students’ motivation and their confidence in being able to complete a task – known as self-efficacy – increased.

For foreign language learners, a 2025 study found that university students using AI-driven personalized systems took more pleasure in learning and had less anxiety and more self-efficacy compared with those using traditional methods. A recent cross-cultural analysis with participants from Egypt, Saudi Arabia, Spain and Poland who were studying diverse majors suggested that positive motivational effects are strongest when tools prioritize autonomy, self-direction and critical thinking. These individual findings align with a broader, systematic review of generative AI tools that found positive effects on student motivation and engagement across cognitive, emotional and behavioral dimensions.

A forthcoming meta-analysis from my team at the University of Alabama, which synthesized 71 studies, echoed these patterns. We found that generative AI tools on average produce moderate positive effects on motivation and engagement. The impact is larger when tools are used consistently over time rather than in one-off trials. Positive effects were also seen when teachers provide scaffolding, when students maintain agency in how they use the tool, and when the output quality is reliable.

But there are caveats. More than 50 of the studies we reviewed did not draw on a clear theoretical framework of motivation, and some used methods that we found were weak or inappropriate. This raises concerns about the quality of the evidence and underscores how much more careful research is needed before one can say with confidence that AI nurtures students’ intrinsic motivation rather than just making tasks easier in the moment.

When AI backfires

There is also research that paints a more sobering picture. A large study of more than 3,500 participants found that while human–AI collaboration improved task performance, it reduced intrinsic motivation once the AI was removed. Students reported more boredom and less satisfaction, suggesting that overreliance on AI can erode confidence in their own abilities.

Another study suggested that while learning achievement often rises with the use of AI tools, increases in motivation are smaller, inconsistent or short-lived. Quality matters as much as quantity. When AI delivers inaccurate results, or when students feel they have little control over how it is used, motivation quickly erodes. Confidence drops, engagement fades and students can begin to see the tool as a crutch rather than a support. And because there are not many long-term studies in this field, we still do not know whether AI can truly sustain motivation over time, or whether its benefits fade once the novelty wears off.

Not all AI tools work the same way

The impact of AI on student motivation is not one-size-fits-all. Our team’s meta-analysis shows that, on average, AI tools do have a positive effect, but the size of that effect depends on how and where they are used. When students work with AI regularly over time, when teachers guide them in using it thoughtfully, and when students feel in control of the process, the motivational benefits are much stronger.

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We also saw differences across settings. College students seemed to gain more than younger learners, STEM and writing courses tended to benefit more than other subjects, and tools designed to give feedback or tutoring support outperformed those that simply generated content.

There is also evidence that general-use tools like ChatGPT or Claude do not reliably promote intrinsic motivation or deeper engagement with content, compared to learning-specific platforms such as ALEKS and Khanmigo, which are more effective at supporting persistence and self-efficacy. However, these tools often come with subscription or licensing costs. This raises questions of equity, since the students who could benefit most from motivational support may also be the least likely to afford it.

These and other recent findings should be seen as only a starting point. Because AI is so new and is changing so quickly, what we know today may not hold true tomorrow. In a paper titled The Death and Rebirth of Research in Education in the Age of AI, the authors argue that the speed of technological change makes traditional studies outdated before they are even published. At the same time, AI opens the door to new ways of studying learning that are more participatory, flexible and imaginative. Taken together, the data and the critiques point to the same lesson: Context, quality and agency matter just as much as the technology itself.

Why it matters for all of us

The lessons from this growing body of research are straightforward. The presence of AI does not guarantee higher motivation, but it can make a difference if tools are designed and used with care and understanding of students’ needs. When it is used thoughtfully, in ways that strengthen students’ sense of competence, autonomy and connection to others, it can be a powerful ally in learning.

But without those safeguards, the short-term boost in performance could come at a steep cost. Over time, there is the risk of weakening the very qualities that matter most – motivation, persistence, critical thinking and the uniquely human capacities that no machine can replace.

For teachers, this means that while AI may prove a useful partner in learning, it should never serve as a stand-in for genuine instruction. For parents, it means paying attention to how children use AI at home, noticing whether they are exploring, practicing and building skills or simply leaning on it to finish tasks. For policymakers and technology developers, it means creating systems that support student agency, provide reliable feedback and avoid encouraging overreliance. And for students themselves, it is a reminder that AI can be a tool for growth, but only when paired with their own effort and curiosity.

Regardless of technology, students need to feel capable, autonomous and connected. Without these basic psychological needs in place, their sense of motivation will falter – with or without AI.

Yurou Wang, Associate Professor of Educational Psychology, University of Alabama

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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AI is changing who gets hired – what skills will keep you employed?

Murugan Anandarajan, Drexel University

The consulting firm Accenture recently laid off 11,000 employees while expanding its efforts to train workers to use artificial intelligence. It’s a sharp reminder that the same technology driving efficiency is also redefining what it takes to keep a job.

And Accenture isn’t alone. IBM has already replaced hundreds of roles with AI systems, while creating new jobs in sales and marketing. Amazon cut staff even as it expands teams that build and manage AI tools. Across industries, from banks to hospitals and creative companies, workers and managers alike are trying to understand which roles will disappear, which will evolve and which new ones will emerge.

I research and teach at Drexel University’s LeBow College of Business, studying how technology changes work and decision-making. My students often ask how they can stay employable in the age of AI. Executives ask me how to build trust in technology that seems to move faster than people can adapt to it. In the end, both groups are really asking the same thing: Which skills matter most in an economy where machines can learn?

To answer this, I analyzed data from two surveys my colleagues and I conducted over this summer. For the first, the Data Integrity & AI Readiness Survey, we asked 550 companies across the country how they use and invest in AI. For the second, the College Hiring Outlook Survey, we looked at how 470 employers viewed entry-level hiring, workforce development and AI skills in candidates. These studies show both sides of the equation: those building AI and those learning to work with it.

AI is everywhere, but are people ready?

More than half of organizations told us that AI now drives daily decision-making, yet only 38% believe their employees are fully prepared to use it. This gap is reshaping today’s job market. AI isn’t just replacing workers; it’s revealing who’s ready to work alongside it.

Our data also shows a contradiction. While many companies now depend on AI internally, only 27% of recruiters say they’re comfortable with applicants using AI tools for tasks such as writing resumes or researching salary ranges.

In other words, the same tools companies trust for business decisions still raise doubts when job seekers use them for career advancement. Until that view changes, even skilled workers will keep getting mixed messages about what “responsible AI use” really means.

In the Data Integrity & AI Readiness Survey, this readiness gap showed up most clearly in customer-facing and operational jobs such as marketing and sales. These are the same areas where automation is advancing quickly, and layoffs tend to occur when technology evolves faster than people can adapt.

At the same time, we found that many employers haven’t updated their degree or credential requirements. They’re still hiring for yesterday’s resumes while, tomorrow’s work demands fluency in AI. The problem isn’t that people are being replaced by AI; it’s that technology is evolving faster than most workers can adapt.

Fluency and trust: The real foundations of adaptability

Our research suggests that the skills most closely linked with adaptability share one theme, what I call “human-AI fluency.” This means being able to work with smart systems, question their results and keep learning as things change.

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Across companies, the biggest challenges lie in expanding AI, ensuring compliance with ethical and regulatory standards and connecting AI to real business goals. These hurdles aren’t about coding; they’re about good judgment.

In my classes, I emphasize that the future will favor people who can turn machine output into useful human insight. I call this digital bilingualism: the ability to fluently navigate both human judgment and machine logic.

What management experts call “reskilling” – or learning new skills to adapt to a new role or major changes in an old one – works best when people feel safe to learn. In our Data Integrity & AI Readiness Survey, organizations with strong governance and high trust were nearly twice as likely to report gains in performance and innovation. The data suggests that when people trust their leaders and systems, they’re more willing to experiment and learn from mistakes. In that way, trust turns technology from something to fear into something to learn from, giving employees the confidence to adapt.

According to the College Hiring Outlook Survey, about 86% of employers now offer internal training or online boot camps, yet only 36% say AI-related skills are important for entry-level roles. Most training still focuses on traditional skills rather than those needed for emerging AI jobs.

The most successful companies make learning part of the job itself. They build opportunities to learn into real projects and encourage employees to experiment. I often remind leaders that the goal isn’t just to train people to use AI but to help them think alongside it. This is how trust becomes the foundation for growth, and how reskilling helps retain employees.

The new rules of hiring

In my view, the companies leading in AI aren’t just cutting jobs; they’re redefining them. To succeed, I believe companies will need to hire people who can connect technology with good judgment, question what AI produces, explain it clearly and turn it into business value.

In companies that are putting AI to work most effectively, hiring isn’t just about resumes anymore. What matters is how people apply traits like curiosity and judgment to intelligent tools. I believe these trends are leading to new hybrid roles such as AI translators, who help decision-makers understand what AI insights mean and how to act on them, and digital coaches, who teach teams to work alongside intelligent systems. Each of these roles connects human judgment with machine intelligence, showing how future jobs will blend technical skills with human insight.

That blend of judgment and adaptability is the new competitive advantage. The future won’t just reward the most technical workers, but those who can turn intelligence – human or artificial – into real-world value.

Murugan Anandarajan, Professor of Decision Sciences and Management Information Systems, Drexel University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter.  https://stmdailynews.com/the-knowledge/