Intelligent Tutoring Systems Need Teachers

ABSTRACT Background The development and distribution of digital learning software, such as intelligent tutoring systems (ITSs), has evolved into a billion‐dollar industry, impacting a vast number of students worldwide. A large number of studies on ITSs have focused on their effects on learning outcomes. However, less is known about student engagement and dropout when ITSs are used in real classroom settings over extended periods, particularly with regard to how these patterns may be linked to different assignment scenarios within ITSs. Objective The present study aimed to explore whether student engagement and dropout varied based on whether mathematics problems within the ITS were assigned by teachers or self‐assigned by students. Methods We evaluated rich data from an ITS for learning mathematics used in Germany and the Netherlands (~139,000,000 problems; n ~ 194,000 students) between 2016 and 2023. To examine whether students' engagement and dropout, both within and between students, varied based on the two assignment scenarios (teacher assigned vs. self‐assigned problems), we employed regression and survival analyses. Results and Conclusions Our results revealed that, in both Germany and the Netherlands, students with teacher‐assigned problems consistently (i) dropped out later, (ii) were active for significantly more weeks and (iii) worked through more mathematics problems each week than those who self‐assigned problems. The results were robust across academic school years in the examined period. Overall, our study raises questions about the use of digital learning software as a stand‐alone solution and suggests embedding such software in real‐life learning scenarios involving teachers.

Authors

Institutions

Publication Details

Journal
Journal of Computer Assisted Learning
Published
2025-12-17
DOI
https://doi.org/10.1002/jcal.70159
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Tutoring Systems Need Teachers

Sebastian Musslick, Younes Strittmatter, Korbinian Moeller, Lisa Bardach et al.
Journal of Computer Assisted Learning
Intelligent Tutoring Systems and Adaptive Learning
article

Intelligent Tutoring Systems Need Teachers

Sebastian Musslick, Younes Strittmatter, Korbinian Moeller, Lisa Bardach, Jennifer Meyer, Markus Spitzer, Miguel Ruíz-García
article en

Abstract

ABSTRACT Background The development and distribution of digital learning software, such as intelligent tutoring systems (ITSs), has evolved into a billion‐dollar industry, impacting a vast number of students worldwide. A large number of studies on ITSs have focused on their effects on learning outcomes. However, less is known about student engagement and dropout when ITSs are used in real classroom settings over extended periods, particularly with regard to how these patterns may be linked to different assignment scenarios within ITSs. Objective The present study aimed to explore whether student engagement and dropout varied based on whether mathematics problems within the ITS were assigned by teachers or self‐assigned by students. Methods We evaluated rich data from an ITS for learning mathematics used in Germany and the Netherlands (~139,000,000 problems; n ~ 194,000 students) between 2016 and 2023. To examine whether students' engagement and dropout, both within and between students, varied based on the two assignment scenarios (teacher assigned vs. self‐assigned problems), we employed regression and survival analyses. Results and Conclusions Our results revealed that, in both Germany and the Netherlands, students with teacher‐assigned problems consistently (i) dropped out later, (ii) were active for significantly more weeks and (iii) worked through more mathematics problems each week than those who self‐assigned problems. The results were robust across academic school years in the examined period. Overall, our study raises questions about the use of digital learning software as a stand‐alone solution and suggests embedding such software in real‐life learning scenarios involving teachers.

Journal of Computer Assisted LearningVol. 42(1)
Universidad Complutense de Madrid (ES), Loughborough University (GB), Osnabrück University (DE), Kaunas University of Technology (LT), Justus-Liebig-Universität Gießen (DE), Princeton University (US), Brown University (US), Luther University (KR), Loughborough College (GB), Pädagogische Hochschule Wien (AT), Giessen School of Theology (DE), Martin Luther University Halle-Wittenberg (DE), University of Tübingen (DE)
Openalex Percentile: Top 78%
Intelligent Tutoring Systems and Adaptive Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.