Social Studies Pre-service Teachers’Probing of Global Issues using Generative AI

This study examined pre-service social studies teachers’ experiences in AI-aided global issues exploration using generative AI in teacher education courses. Adopting a mixed-method research approach, quantitative and qualitative analysis were conducted on the reports created by 66 pre-service teachers majoring in social studies at four teacher education universities, who participated in the “Exploring Global Issues Using Generative AI” program. The findings show that the international issues that pre-service teachers sought to explore through generative AI tended to converge on topics with high timeliness and strong regional proximity. In the AI-generated textual and visual outputs, four prominent characteristics were identified: neutrality, selectivity, bias, and hallucination. While the pre-service teachers recognized the educational utility of generative AI in providing synthesized and structured information, rapid image generation, and interactive support for deeper inquiry, they also pointed out limitations such as informational inaccuracy, implicit bias, and the risk of uncritical acceptance of AI outputs. Furthermore, the neutral stance of generative AI on politically sensitive topics and its differential responses depending on question formulation highlighted the need for AI literacy education. This study draws critical attention to the pedagogical potential of generative AI in social studies and provides practical insights for future AI-integrated teacher education programs.