AI for adaptive science teaching: strengthening teacher self-efficacy and perceived usefulness

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 education science 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.