# Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis

arXiv: https://arxiv.org/abs/2607.08748 · HTML: https://mlsanity.com/paper/2607.08748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 9 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Revolutionising Distance Learning: A Comparative Study of Learning Progress with AI-Driven Tutoring](https://mlsanity.com/paper/2403.14642/) (2024)
- [Understanding University Students' Use of Generative AI: The Roles of Demographics and Personality Traits](https://mlsanity.com/paper/2505.02863/) (2025)
- [Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals](https://mlsanity.com/paper/2508.04070/) (2025)

## Abstract (arXiv)

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.