Approximately 90% of 1,100 U.S. students surveyed at two-year and four-year colleges in 2025 reported using generative AI for everything from drafting assignments to clarifying complex concepts.
But when students use AI as a tutor or study partner, not as an immediate answer generator, does it make it easier or harder for them to learn?
We named the tool Macro Buddy and trained it to guide some students at one of our undergraduate macroeconomics classes at the University of Wisconsin, La Crosse, through their reasoning rather than giving them direct answers.
We found in our research, conducted in spring 2025, that students who used Macro Buddy, alongside peer discussion, earned higher exam scores than students who worked alone, without this AI tutor.
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One of our macroeconomics courses enrolled 140 undergraduate students, mostly in their first or second year of college, divided across four sections.
Students’ course materials, assignments and exams were identical across all four sections. Students were generally not allowed to use AI tools or collaborate with classmates during exams. Students took all tests in person and were not allowed to reference any notes or other materials during the exam.
As a result, exam scores reflected what students understood and could explain on their own – without the help of AI or any other outside source.
After all students took their first exam, we randomly assigned the four class sections to take on a different study format.
We prompted one group of students to work individually, without Macro Buddy; another group of students worked in groups, without Macro Buddy; a third group of students worked individually, with Macro Buddy; and a fourth group of students worked in groups, with Macro Buddy.
We wanted to compare how different study approaches – working alone, working with classmates, using Macro Buddy or combining both – altered how well students did on exams.
Macro Buddy’s skills
We trained Macro Buddy with the help of lecture transcripts, slides and homework questions specifically from this macroeconomics course.
Macro Buddy had internet access turned off, so it relied only on the instructor’s course materials.
Macro Buddy was designed to act like a tutor, not an answer machine. Instead of giving students complete solutions, Macro Buddy asked follow-up questions meant to guide students toward an answer.
For example, if a student asked why lower prices might increase consumers’ spending, Macro Buddy would not offer a quick, full explanation. It might instead ask what happens to people’s purchasing power when prices fall. The student would then have to connect the concepts and explain their reasoning, in their own words, step by step.
This distinction between explaining an idea and receiving a finished answer matters.
An AI tool that simply delivers answers can allow students to skip thinking through a problem. One study found that when college students rely on a chatbot as a crutch, they perform worse when they no longer have access to it. A tool that asks questions requires students to do the work themselves, even while receiving guidance. This is the very process that makes learning stick.
What happened to students’ learning
The one group of students that continued working individually, without AI, served as our control group.
The other three groups changed how they studied: One began working in groups without AI, one worked individually with Macro Buddy, and the last group combined group work with Macro Buddy.
All of the students’ average scores declined when they took their second exam, across all four study groups.
By the third exam, however, differences across sections became clearer.
Students who used both Macro Buddy and group discussion earned the highest average scores. Students who used Macro Buddy alone also scored higher than those who worked alone without Macro Buddy. Students who worked in groups without Macro Buddy sh