Development and implementation of a generative artificial intelligence-enhanced simulation to enhance problem-solving skills for pre-service teachers

Effective teachers should be equipped to solve complex problems across diverse instructional and learning contexts. However, many teacher training programs struggle to bridge the gap between theoretical knowledge to real-world applications. The current study tackles this challenge by developing a generative artificial intelligence (GenAI)-enhanced simulation to improve preservice teachers’ problem-solving abilities. Using design-based research (DBR), we created a virtual environment that integrates problem-based learning (PBL) with GenAI technology. The simulation was rigorously refined through expert review and usability testing before being implemented in a teacher training program. We evaluated its effectiveness by comparing three groups: (1) a text-based scenario, (2) a rule-based simulation, and (3) a GenAI-enhanced simulation. Pre- and post-test results showed significant improvements in problem-solving skills for both the rule-based and GenAI-enhanced simulation groups compared to the text-based scenario group. Notably, qualitative findings revealed that students reported heightened realism and immersion in the GenAI-enhanced simulation, attributing this to more dynamic interactions with AI agents that helped them better contextualize PBL and increased their motivation. Our study findings contribute design principles for developing GenAI-enhanced simulations in teacher education, offering promising insights into leveraging AI technology to create more engaging and effective training experiences.