Behind the OneTutor Learning Report: why and how we built it
After reading this article you will know three things: why we built the OneTutor Learning Report, how we built it, and what we plan to do with it next.
First, who we are. OneTutor gives instructors the tools to shape how their students use AI while they learn. It works in three steps.
- Set up. Instructors turn their own course material into an AI tutor for their students.
- Practice. They add quiz questions for formative assessment. After answering, students can ask with one click why their answer was right or wrong. That click sends a message to the tutor, which is why quiz feedback shows up in the report.
- See. Instructors see how their students study: the questions they ask and the answers the tutor gives.
Instructors usually cannot see what their students do with AI. With a tool like OneTutor, they can.
OneTutor is used at more than 50 institutions: universities, private academies and companies. This first report covers only universities and universities of applied sciences, whose courses share a similar rhythm and similar learning goals. Adding corporate training would mean comparing apples and oranges.
You can read the report itself at onetutor.ai/learning-report. This article is the story behind it.
Why we publish it
Less struggle is a paradox
Since ChatGPT arrived in November 2022, learning has become one of the main reasons people use AI. The benefits are obvious: AI is available at any hour, adapts to each student and takes the struggle out of studying.
That last benefit is a paradox. Less struggle can help a student grasp a new concept, and it can quietly take over the thinking that learning depends on. In a field experiment in high school math, students with unrestricted access to GPT did 48% better on practice problems but 17% worse in the exam. A tutor version with guardrails largely avoided the harm (Bastani et al., PNAS, 2025).
Not whether, but how
So the question is not whether students use AI, but how. Does a student hand over the thinking, copy an answer and feel good about ticking off a chapter? Or does the student reflect on their own thoughts, challenge their progress and find the process hard? That is what this report tries to find out. We want to understand how students interact with an AI tutor at university, which use cases dominate, how usage changes over a semester and how it differs between academic disciplines.
Why we share it publicly
We look at this data to understand the status quo, and we want it to be the basis for decisions on how AI should be used for learning. We believe the effectiveness of AI in learning has to be evaluated, so that the learning process can be steered toward the benefits and away from the risks.
By sharing what we find and how we read it, we want to offer a perspective and open a constructive discussion with everyone who is rethinking their teaching.
How we built it
The data
We classified all 628,000 anonymized student messages from the winter semester 2025/26 and the summer semester 2026 at 32 universities and universities of applied sciences in Germany, Austria and Czechia. There is no sampling. Demo and test accounts, schools, companies and training providers are left out.
Four guiding questions
Four questions guided the analysis. Each one has its own section in the report.
- When do students use AI to study during a semester?
- How do academic disciplines differ in how they use it?
- What do students use AI for while studying?
- Who does the cognitive work: the student or the AI?
Weeks and academic disciplines
To make the semesters of different universities comparable, we count weeks from each university's own lecture start. The count runs on through breaks and exams.
To find out which academic discipline students learn in, we used the name of the course. A language model assigned each course name to one of thirteen academic disciplines: Law, Business and Economics, Social Sciences, Psychology, Education, Humanities and Languages, Mathematics, Computer Science, Natural Sciences, Engineering, Medicine and Health, Agriculture, Food and Life Sciences, and Public Administration. Interdisciplinary courses count in the totals but get no row of their own.
To protect courses and institutions, a value is only shown when at least 5 courses, 2 institutions and 500 messages stand behind it.
Eleven request types, one question
To tell what students actually do, we defined eleven request types. A language model sorts each message into one of them, using a fixed coding guide built around one question: does the student or the AI do the thinking? We then group the types into four, following the ICAP framework:
- Students think (ICAP: interactive or constructive): check an own solution, answer the tutor with reasoning, ask a targeted question.
- Practice (ICAP: active): give a short quiz answer, ask why a quiz answer was right or wrong, ask to be quizzed.
- AI thinks (ICAP: passive): ask for an explanation, look up a fact, have a task solved, have material created.
- No learning request: greetings, thanks, organizational or technical questions.
Why ICAP and not Bloom
We tried Bloom's taxonomy first and dropped it. Based purely on the content of a single message, it is hard to identify the right Bloom level. A short request like “make me a quiz” would land on the top level, create, although the student creates nothing. The four ICAP levels (Chi and Wylie, Educational Psychologist, 2014) are much easier to tell apart: does the student bring their own thinking, practice, take in what the AI produces, or make no learning request at all?
How this compares to other studies
Our data covers one AI tutor inside university courses. Other studies look at general chatbots or at learning outcomes. Taken together, they point in the same direction as this report: what matters is how AI is used.
Learning is one of the main uses of AI. 10.2% of all ChatGPT messages ask for tutoring or teaching, in a sample of about 1.1 million conversations from May 2024 to June 2025 (Chatterji et al., How People Use ChatGPT, OpenAI, September 2025).
Bloom describes the AI, not the student. Anthropic's education report rated Claude's answers in student conversations by Bloom's taxonomy: creating (40%) and analyzing (30%) came out on top. The authors note that this describes the AI's work, one more reason why we rate who does the thinking instead. They also found nearly half of the conversations to be direct, aimed at getting answers or content with little engagement. A different method, and a picture similar to our 54% of messages where the AI does the thinking. In their data, computer science made up 39% of student conversations, against 5% of US bachelor's degrees. In our data, the mix of academic disciplines mostly follows which instructors brought the tutor into their courses (Anthropic Education Report: How University Students Use Claude, April 2025).
Guardrails matter. In the high school math experiment mentioned above, students did 48% better on practice problems with unrestricted GPT but 17% worse in the exam once access was gone (Bastani et al., Generative AI without guardrails can harm learning, PNAS, 2025).
Our framework. ICAP (Chi and Wylie, 2014) is the lens for everything above. Our view on AI tutors in teaching is in the OneTutor whitepaper.
What the report can't tell you yet, and what comes next
Five limits of this edition
This first edition has clear shortcomings. We publish it anyway, because it is a good starting point for a conversation, and we will improve it with every edition.
- A few places shape the totals. The five largest universities send 82% of all messages, and the ten largest courses almost a quarter (24%). Within courses, a smaller group of very active students sends many of the messages.
- No learning outcomes yet. We see how students use the tutor, not whether it improved their understanding or their grades. The accompanying research by the bidt on the use of OneTutor in the summer semester 2025 shows how hard this is: it found no difference in perceived learning success between frequent users, light users and students who did not use the tutor, and exam results could not be linked for data protection reasons.
- Only one side of the conversation. We classify what students write, without the tutor's replies. Real back and forth is therefore likely undercounted.
- AI labels are not perfectly reproducible. A language model sorts every message using a fixed coding guide. A second run would label some messages differently.
- Quiz results are not included yet. How students perform on quiz questions is one of the next data points we want to add.
What comes next
We will publish this report every semester to follow how the use of AI in teaching and learning develops. We also want to measure better: quiz performance, the tutor's replies and, where possible, learning outcomes.
The results already give us two directions for OneTutor. Students mostly use AI to retrieve information and take it in passively, so we want to change the tool and how it is deployed to promote productive struggle, moving learning from passive to active, constructive and interactive. And quiz questions already move students from passive to active, but mostly around the exams, so we are looking for ways to keep practice going through the whole semester.
Tell us where we read it wrong
This edition is a starting point, not a verdict. Dissect our approach, question our readings and tell us what we missed. The goal is to find out together how and when AI improves learning and teaching. You can write to us here.
If you want the next edition first, subscribe to our newsletter. A new edition comes every semester, with what changed since the last one.
