Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
For higher education researchers and developers, this large-scale analysis fills a gap in understanding actual usage of educational chatbots, but the findings are descriptive and incremental.
This study analyzes usage patterns of an AI-based learning assistant (Syntea) using log data from 77,543 students, finding that usage varies across demographic and structural contexts, providing empirical evidence for further development.
In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education. Based on objective log data from 77,543 students enrolled in distance studies, we examine usage patterns across gender, age group, study cluster, degree, and study mode. To date, existing research on educational chatbots has largely relied on comparatively small samples and self-reported survey data, while large-scale evidence on actual usage behavior remains limited. Our findings show that Syntea is already embedded in the study routines of many learners, but that usage differs across demographic and structural contexts. By identifying these patterns, our study provides an empirical basis for the further development of AI-based learning support and contributes a large-scale analysis of educational chatbot usage in higher education.