arXiv subjects

Quintus Stierstorfer

Publications and source records attributed to Quintus Stierstorfer.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.AI↗

arXiv2025

Personalized Knowledge Transfer Through Generative AI: Contextualizing Learning to Individual Career Goals

As artificial intelligence becomes increasingly integrated into digital learning environments, the personalization of learning content to reflect learners' individual career goals offers promising potential to enhance engagement and long-term motivation. In our study, we investigate how career goal-based content adaptation in learning systems based on generative AI (GenAI) influences learner engagement, satisfaction, and study efficiency. The mixed-methods experiment involved more than 4,000 learners, with one group receiving learning scenarios tailored to their career goals and a control group. Quantitative results show increased session duration, higher satisfaction ratings, and a modest reduction in study duration compared to standard content. Qualitative analysis highlights that learners found the personalized material motivating and practical, enabling deep cognitive engagement and strong identification with the content. These findings underscore the value of aligning educational content with learners' career goals and suggest that scalable AI personalization can bridge academic knowledge and workplace applicability.

cs.AI↗

arXiv2024

Revolutionising Distance Learning: A Comparative Study of Learning Progress with AI-Driven Tutoring

Generative AI is expected to have a vast, positive impact on education; however, at present, this potential has not yet been demonstrated at scale at university level. In this study, we present first evidence that generative AI can increase the speed of learning substantially in university students. We tested whether using the AI-powered teaching assistant Syntea affected the speed of learning of hundreds of distance learning students across more than 40 courses at the IU International University of Applied Sciences. Our analysis suggests that using Syntea reduced their study time substantially--by about 27\% on average--in the third month after the release of Syntea. Taken together, the magnitude of the effect and the scalability of the approach implicate generative AI as a key lever to significantly improve and accelerate learning by personalisation.

cs.CY↗