ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Education
AI for Adaptive Science Teaching: Strengthening Teacher Self-Efficacy and Perceived Usefulness
Mathea Brückner 1
Christoph Thyssen 2
Johannes Huwer 1
1. University of Konstanz, Konstanz, Germany
2. Padagogische Hochschule Freiburg, Freiburg, 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
Abstract
Integrating Artificial Intelligence (AI) into everyday school practice holds great potential for implementing adaptive teaching. AI-supported tools enable learning processes to be individualized and facilitate a more effective consideration of students' diverse needs. However, to realize these benefits, adequate technical infrastructure, teachers' willingness, and relevant competencies are essential. This pilot study investigates whether a short, targeted intervention can enhance science teachers' Artificial Intelligence Self-Efficacy Expectation (AISEE) and their Perceived Usefulness (PU) of AI in adaptive science teaching. In addition, teachers' conceptual understanding of the adaptive teaching components 'assessment', 'feedback', and 'adaptivity' was examined by asking them to provide descriptive terms for each component. Their responses were analyzed and inductively categorized to gain deeper insights into teachers' understanding of the concepts. The participants were German lower secondary educationscience teachers in multiplier roles. The results show a significant increase in both PU and AISEE after the intervention and a post-intervention correlation between these two variables. The results underscore the value of hands-on training formats in fostering Self-Efficacy (SE) and PU for AI-supported adaptive science teaching.
Summary
Keywords
adaptivity, artificial intelligence, stem education, teacher training, technology acceptance
Received
02 December 2025
Accepted
18 February 2026
Copyright
© 2026 Brückner, Thyssen and Huwer. 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: Mathea Brückner; Johannes Huwer
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.