AI-supported data analysis boosts student motivation and reduces stress in physics education

TL;DR

This study shows that AI-supported data analysis in physics education enhances student engagement and motivation without compromising learning outcomes, using a GPT-based chatbot to support experimental data analysis.

Contribution

It introduces a custom GPT-based AI tool for supporting physics data analysis and demonstrates its positive impact on emotional and motivational responses in student teachers.

Findings

AI group reported higher engagement and enjoyment

Both groups showed significant learning gains

No significant difference in cognitive performance between groups

Abstract

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, with both groups completing identical tasks in a guided setting. Learning progress was measured using pre- and post-tests, while emotional and motivational variables were assessed through structured surveys. Both groups demonstrated significant learning gains, with no statistically significant differences found between them in terms of cognitive performance. However, the AI group reported substantially higher levels of engagement, enjoyment, and perceived method effectiveness compared to the Excel group. These findings suggest that interactive AI tools may enhance the affective dimensions of learning, even when cognitive outcomes remain comparable to traditional methods. The results underscore the importance of integrating AI not as a replacement for instructional design, but as a supportive element within pedagogical frameworks. Future research should explore long-term retention effects, the role of learner diversity, and comparisons with other forms of pedagogical support.