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Over 80% of Middlebury College students use generative AI for coursework, according to a recent survey I conducted with my colleague and fellow economist Zara Contractor. This is one of the fastest technology adoption rates on record, far outpacing the 40% adoption rate among U.S. adults, and it happened in less than two years after ChatGPTs public launch. Although we surveyed only one college, our results align with similar studies, providing an emerging picture of the technologys use in higher education. Between December 2024 and February 2025, we surveyed over 20% of Middlebury Colleges student body, or 634 students, to better understand how students are using artificial intelligence, and published our results in a working paper that has not yet gone through peer review. What we found challenges the panic-driven narrative around AI in higher education and instead suggests that institutional policy should focus on how AI is used, not whether it should be banned. Not just a homework machine Contrary to alarming headlines suggesting that ChatGPT Has Unraveled the Entire Academic Project and AI Cheating Is Getting Worse, we discovered that students primarily use AI to enhance their learning rather than to avoid work. When we asked students about 10 different academic uses of AIfrom explaining concepts and summarizing readings to proofreading, creating programming code, and, yes, even writing essaysexplaining concepts topped the list. Students frequently described AI as an on-demand tutor, a resource that was particularly valuable when office hours werent available or when they needed immediate help late at night. We grouped AI uses into two types: augmentation to describe uses that enhance learning, and automation for uses that produce work with minimal effort. We found that 61% of the students who use AI employ these tools for augmentation purposes, while 42% use them for automation tasks like writing essays or generating code. Even when students used AI to automate tasks, they showed judgment. In open-ended responses, students told us that when they did automate work, it was often during crunch periods like exam week, or for low-stakes tasks like formatting bibliographies and drafting routine emails, not as their default approach to completing meaningful coursework. !function(){"use strict";window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}})}(); Of course, Middlebury is a small liberal arts college in Vermont with a relatively large portion of wealthy students. What about everywhere else? To find out, we analyzed data from other researchers covering over 130 universities across more than 50 countries. The results mirror our Middlebury findings: Globally, students who use AI tend to be more likely to use it to augment their coursework, rather than automate it. But should we trust what students tell us about how they use AI? An obvious concern with survey data is that students might underreport uses they see as inappropriate, like essay writing, while overreporting legitimate uses like getting explanations. To verify our findings, we compared them with data from AI company Anthropic, which analyzed actual usage patterns from university email addresses of their chatbot, Claude AI. Anthropics data shows that technical explanations represent a major use, matching our finding that students most often use AI to explain concepts. Similarly, Anthropic found that designing practice questions, editing essays, and summarizing materials account for a substantial share of student usage, which aligns with our results. In other words, our self-reported survey data matches actual AI conversation logs. Why it matters As writer and academic Hua Hsu recently noted, There are no reliable figures for how many American students use AI, just stories about how everyone is doing it. These stories tend to emphasize extreme examples, like a Columbia student who used AI to cheat on nearly every assignment. But these anecdotes can conflate widespread adoption with universal cheating. Our data confirms that AI use is indeed widespread, but students primarily use it to enhance learning, not replace it. This distinction matters: By painting all AI use as cheating, alarmist coverage may normalize academic dishonesty, making responsible students feel naive for following rules when they believe everyone else is doing it. Moreover, this distorted picture provides biased information to university administrators, who need accurate data about actual student AI usage patterns to craft effective, evidence-based policies. Whats next Our findings suggest that extreme policies like blanket bans or unrestricted use carry risks. Prohibitions may disproportionately harm students who benefit most from AIs tutoring functions while creating unfair advantages for rule breakers. But unrestricted use could enable harmful automation practices that may undermine learning. Instead of one-size-fits-all policies, our findings lead me to believe that institutions should focus on helping students distinguish beneficial AI uses from potentially harmful ones. Unfortunately, research on AIs actual learning impacts remains in its infancyno studies Im aware of have systematically tested how different types of AI use affect student learning outcomes, or whether AI impacts might be positive for some students but negative for others. Until that evidence is available, everyone interested in how this technology is changing education must use their best judgment to determine how AI can foster learning. Germán Reyes is an assistant professor of economics at Middlebury. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Being yourself is not always an easy taskespecially at work. But new research finds the ability to do so comes easier to some than to others, for a surprising reason. The ability to be authentic on the job also has a great deal to do with how well-liked you are. In a series of studies involving thousands of participants, social psychologists at Columbia Business School found that social status (defined as how well liked someone is) is deeply important when it comes to being yourself. Our findings suggest that social status may be as important as self-esteem in increasing authenticity, which is surprising, said Erica R. Bailey, a Ph.D. student who worked on the studies, said. Dr. James T. Carter, another one of the researchers and an assistant professor of organizational behavior at Cornell University, told Fast Company that the study used Bailey’s previous work, which developed ways to quantify authenticity. The studies involved conversations between strangers which took place over Zoom. The researchers then manipulated social status in follow up experiments. Researchers created scenarios, such as one where participants were told if they were selected or not for Employee of the Month, which was based on being “well-respected and admired by others.” They were asked to write about how the experience made them feel. Carter said the experiments showed that social status increased “felt and expressed authenticity.” The experiments seemed to indicate that popularity matters deeply (even long after high school). So much so, Carter says, that it’s an even more important factor than rank or position when it comes to being able to show up as yourself at work. “This is interesting from a research standpoint because prior work would argue that formal rank (or power) is critically important for authenticity, but we find the story is a bit more complicated than that,” Carter said. Carter added, Although both are relevant for authenticity, it is social status that really lets people be their authentic selves.” The ability to feel comfortable being authentic at work matters. Previous research has found that authenticity is a huge driver of happiness. A 2020 meta analysis found that authenticity is key to employee engagement and overall well-being. The latest research suggests that high school guidance counselors were wrong. Popularity does matter even after high school. In fact, it may be one of the most powerful tools one has at the office. And, perhaps, in life.
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If it feels harder to get people to accept your ideas in virtual meetings its not surprising. When so much communication is virtual, reaching and inspiring people with your thinking is more difficult than ever. Want to reach your boss, customers, or colleagues? This article presents the steps needed to get across your ideas in meetings. #1 KEEP THE CAMERAS ON First, urge everyone to keep their cameras on. In most meetings, maintaining visual contact is the norm. But if this is not the practice, urge the moderator or your colleagues to keep their cameras on. Body language and facial gestures play a big role in persuasion. Seeing others allows you to read your audience. Strong body language and eye contact on your part will strengthen your pitch. #2 CHOOSE THE RIGHT MOMENT Second, if you are going to get buy-in for an idea, broach your topic at the best time. If its a formal meeting, be sure to get yourself on the agenda. People will take your views more seriously if they see you have been allotted time on the agenda. But if the meeting is more informal, listen carefully for the right moment. For example, if your boss has said I want to assign you another client, dont rush in with your defensive reply, but wait for your boss to explain. Or if you are selling to a customer and the issue of pricing comes up, let your customer share their thinking about costs before you jump in. The more input you can get from others, the better your response will be. #3: BEGIN BY BRIDGING Third, begin your pitch with a reference to what others have said. This will show that you have heard and respect the views of others. If its a formal meeting and you have prepared your remarks in advance, begin by creating a context for your views. Acknowledge the input of colleagues and credit those who have made your proposal possible. Bridge, as well, by building on the exchanges at the meeting and crediting your colleagues for their ideas. For example: Our discussion has focused on the new hiring practices. And the HR team has done a great job defining these. Now Id like to suggest how we can get buy-in from the support staff. Avoid adversarial relationships and bridging words such as but, however, In contrast, or I dont agree. Find common ground. #4 MAKE YOUR POINT SUCCINCTLY Fourth, keep your remarks short and to the point. After bridging from what others have said, present your message in one sentence (and only one sentence!). It is often an I believe statement or something that clearly positions your thinking. For example: I believe that we should offer this client a second option. Or My view is that we should proceed with a more cost-efficient plan. Follow the message with proof points. These are arguments that support your message. There are several ways you can prove your message: (1) Give reasons; (2) Show ways to implement your plan; (3) Present an example that shows why your pitch is important. #5: END WITH A CALL TO ACTION Fifth, end with a call to action. Indicate the steps that need to be taken to implement your idea. It could be the action you want to take, steps you want your audience to follow, or a plan youll undertake together. Whatever the action, it should be something that your entire pitch has pointed to. With the call to action, you are moving your audience toward something tangible that youd like to see happen. With this final element, your pitch becomes actionable. You can find a full discussion of impromptu meeting scripts in my book Impromptu: Leading in the Moment.
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