ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Education
This article is part of the Research TopicGenerative AI Tools & Software for EducationView all articles
AI-Supported Data Analysis Boosts Student Motivation and Reduces Stress in Physics Education
Provisionally accepted
- University of Cologne, Cologne, Germany
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The integration of artificial intelligence (AI) into education presents new opportunities for supporting learning processes. This study investigates the impact of AI-assisted versus traditional Excel-based data analysis on both learning outcomes and emotional-motivational responses in a physics education context. A custom GPT-based chatbot, ExperiMentor, was developed to support student teachers in analyzing experimental data from thread and spring pendulum experiments. Fifty student teachers were randomly assigned to either the AI or Excel group. Both groups completed identical tasks in a guided setting, and learning progress was measured using pre- and post-tests. Emotional and motivational variables were assessed through structured surveys. While both groups demonstrated learning gains, no statistically significant differences were found between them in terms of performance. However, the AI group showed substantially higher levels of engagement, enjoyment, and perceived method effectiveness. These differences suggest that interactive AI tools may enhance the learning experience, even when cognitive outcomes are comparable to those achieved with traditional tools. The results underscore the importance of integrating AI not as a replacement for instructional design, but as a supportive element within it. Future research should explore long-term effects, learner diversity, and comparisons with other pedagogical supports.
Keywords: artificial intelligence, data analysis, Excel, Learning, Motivation, physics education
Received: 06 Oct 2025; Accepted: 12 Jan 2026.
Copyright: © 2026 Henze, Lademann, Bresges and Becker-Genschow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jannik Henze
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.