Enhancing teacher AI literacy and integration through different types of cases in teacher professional development

Abstract

Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing students for a technology-centric future. This study examined the influence of a case-based AI professional development (PD) program on AI integration strategies and AI literacy among seven middle school science teachers. Employing three distinct case problems, from well-structured to ill-structured, the AI PD program aimed to stimulate teachers’ reflection on AI literacy development and encourage the construction of problem-solving and AI integration strategies within various pedagogical contexts. Analysis of video-recorded case discussions revealed that teachers primarily drew on personal experiences for collaborative problem-solving across the three cases. However, the complexity of the case problems influenced their approach to knowledge co-construction, and dealing with ill-structured problems promoted the application of new knowledge. Through analyzing the survey data, we found a marked increase in teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting a pivotal role for direct instruction that supports AI literacy growth. However, their application of this AI knowledge was limited during the case discussions, while other domains of teacher AI literacy were more frequently employed. The findings highlight the importance of combining direct instruction with case-based discussions in AI-related PD programs to bolster teacher AI literacy effectively. The research has implications for using a case-based learning approach during short-term PD initiatives and advocates the ongoing need for comprehensive AI literacy development to facilitate teachers’ AI integration in subject-specific teaching.

Keywords

• AI integration
• Teacher professional development
• Case problems
• Cased-based discussion
• Teacher AI literacy