Empowering preservice teachers’ AI literacy: Current understanding, influential factors, and strategies for improvement

Artificial Intelligence (AI) holds significant potential in transforming future educational practices by enhancing teaching and learning. However, its full realization heavily depends on teachers' AI literacy, which significantly influences their engagement levels with AI technologies and their abilities to adopt AI technologies effectively in similar settings. In this study, we conduct a holistic analysis examining pre-service teachers' AI literacy for providing insights about current understanding of AI technologies, perceptions of the applications of AI in education, ethical issues and strategies for effectively enhancing AI literacy. Guided by Bloom's Taxonomy, a comprehensive, layered survey was designed to systematically evaluate pre-service teachers' AI literacy competencies. The Technology Acceptance Model 3 (TAM3) was mapped onto each layer to design questions that identify factors influencing pre-service teachers' AI literacy proficiency. The survey was administered to a sample of 200 pre-service teachers in a large public university. Descriptive analysis and ANOVA were conducted to examine how the influential factors influence AI literacy within and across layers. We also conducted a thematic analysis to identify the most preferred strategies to enhance pre-service teachers' AI literacy. The analytical results revealed a significant gap in understanding levels of AI mechanisms among pre-service teachers and highlighted its positive associations with their engagement, perceptions and ethical considerations of AI in educational settings. The findings also underscored the importance of hands-on, practical training and in-person collaborative learning for enhancing pre-service teachers' AI literacy. Finally, we discussed practical implications regarding implementing training and professional development programs for fostering pre-service teachers' AI literacy.