The Impact of Artificial Intelligence and STEM Applications on Pre-Service Teachers’ Learning Motivation and Individual Innovativeness: A Quasi-Experimental Study

Abstract

The current research aims to examine the effect of AI & STEM applications on pre-service teachers’ learning motivation and individual innovativeness. It was conducted using a quasi-experimental design with experimental and control groups and pre-test–post-test measurements. The study group consisted of 64 undergraduate students taking a teaching practice course at the Faculty of Education of a state university in Almaty, with 32 students from each group. During the six-week application period, AI & STEM-based teaching applications were implemented in the experimental group. The existing curriculum was continued in the control group. The experimental group applications were planned within a three-stage process consisting of AI awareness and STEM integration. An AI-supported design workshop, microteaching, and reflective evaluation sessions. The Learning Motivation Scale, developed by Noe and Wilk (1993) and the Individual Innovation Scale, developed by Hurt, Joseph, and Cook (1977) were used as data collection tools. Since a significant difference was found between the pre-test learning motivation scores of the groups, Analysis of Covariance (ANCOVA) was applied in the post-test comparison. In the comparison of individual innovation, an independent samples t-test was used. The research findings indicate that students in the experimental group who participated in AI&STEM applications achieved statistically significantly higher post-test scores than students in the control group in terms of both learning motivation and individual innovation skills. According to Cohen's classification, the effect size for learning motivation was found to be large (η² = 0.147). This result shows that AI&STEM integration makes a significant contribution to teacher training processes. In this context, its integration into higher education programs is recommended.