Imagine your study app knows when you’re struggling, your chatbot tutor remembers your mistakes, and your university dashboard tracks your every click. These kinds of data-driven tools, part of what researchers call “learning analytics,” promise more personalised education. But there’s a catch: they rely on collecting and analysing student data. So how do students actually feel about that?
A new study co-authored by Mi3 assistant professor Louis Longin explores this question by digging into the psychology behind students’ reactions to these technologies. Are people mainly influenced by who they are, such as their age, background, and general attitudes toward privacy? Or does the situation itself, such as how the data is used, by whom, and for what purpose, matter just as much?
To find out, researchers including collaborators from TUM asked over 250 students to evaluate different hypothetical scenarios involving educational technologies, such as AI chatbots, performance dashboards, and profiling systems. Each scenario tweaked key details: what kind of data was collected, what it was used for, and in which academic context. The goal was to see what shapes students’ willingness to accept and actually use these tools.
The results challenge the idea that privacy attitudes are fixed personality traits. Yes, individual differences matter, especially a tendency to be cautious and concerned about data use. Students who scored high on this trait were consistently less comfortable with data-driven tools and less likely to want to use them.
But here’s the twist: context mattered just as much. The same student might be perfectly fine sharing data with a teacher to improve learning, but uncomfortable if that data is used for broader analytics or shared with third parties. In fact, the study found that situational details, like the purpose of the tool or the type of data collected, explained just as much variation in student attitudes as personal traits did.
Demographics, surprisingly, played a smaller role than you might expect. Age, gender, and education level didn’t strongly predict attitudes on their own, although there were some subtle patterns. For instance, students from certain regions, including parts of Europe and the U.S., were generally less inclined to use these technologies, hinting at cultural differences in how privacy is perceived.
So what does all this mean for the future of education technology? The takeaway is simple but important: there’s no one-size-fits-all solution. Designing ethical and effective learning tools isn’t just about building better algorithms; it’s about giving students more control and making data use transparent and context-sensitive.
Instead of assuming that all students will react the same way, developers and institutions need to create systems that let users decide what data they share, and when. Equally important is involving students in these decisions, helping them understand how their data is used and giving them a voice in the process.
In a world where education is becoming increasingly data-driven, trust may be just as crucial as technology itself.
Check out the original article and Louis Longin’s profile.
References:
Poquet, O., Salta, S., Longin, L., and Viberg, O. (2026). What shapes learner perceptions of LA technologies? Demographics, privacy dispositions and contexts. In Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference (LAK ’26). Association for Computing Machinery, New York, NY, USA, 940–946. https://doi.org/10.1145/3785022.3785102