Transforming Prospective Elementary Teachers' Social Studies Learning through Generative AI in the Digital Era

Abstract

The rapid integration of Artificial Intelligence (AI) into higher education is transforming how prospective elementary school teachers access, interpret, and construct knowledge in Social Studies education. This study examines changes in learning patterns among prospective elementary school teachers in AI-supported Social Studies education, identifies factors associated with these changes, and explores instructional strategies used to maintain critical and reflective learning. Using a qualitative exploratory design, the study was conducted at Universitas Iskandar Muda, Banda Aceh, Indonesia. Data were collected through classroom observations, in-depth interviews with two lecturers and two prospective teachers, and document analysis. Data were analyzed interactively through data reduction, data display, and conclusion drawing. The findings reveal a shift from predominantly literature-based and reflective learning toward faster, AI-assisted information seeking and knowledge production. Participants associated this transformation with technological development, digital learning culture, academic workload, limited academic literacy, generational characteristics, and lecturers’ pedagogical practices. Although AI was perceived to improve the efficiency of information retrieval and broaden access to learning resources, participants also expressed concerns about reduced critical reflection, weaker social argumentation, and less intensive academic interaction when AI was used uncritically. In response, lecturers employed problem-based, inquiry-based, reflective, and collaborative learning, supported by AI literacy development, to encourage verification, interpretation, and critical engagement with AI-generated information. These findings highlight the importance of pedagogically guided AI integration in developing adaptive, critical, reflective, and human-centered Social Studies education.