Development of an activity-based learning model integrated with artificial intelligence for pre-service social studies teachers

This study aimed to develop an activity-based learning model integrated with artificial intelligence (AI) for pre-service social studies teachers. The study employed a research and development (R&D) approach comprising three phases: (1) contextual analysis through document analysis and stakeholder interviews, (2) model design and development based on constructivist learning theory and activity-based learning principles, and (3) expert review of the proposed model. Data were collected using document analysis forms, semi-structured interview protocols with lecturers and students, and an expert evaluation form. The developed instructional model consists of five stages (EFASA Model): Engage and Prompt, Facilitate Interactive Learning, Analyze Data and Make Decisions, Summarize and Reflect, and Assess and Extend Learning. The model integrates activity-based learning with AI-supported tools to facilitate inquiry, data analysis, collaborative problem-solving, and reflective learning processes. In addition, the model includes a social system that emphasizes collaboration and teacher facilitation, a support system that incorporates digital and AI-based resources, and assessment strategies aligned with analytical thinking and decision-making competencies. Expert review results suggest that the model demonstrates a high level of appropriateness, feasibility, utility, and consistency for use in social studies teacher education contexts. The study contributes to instructional design by providing a structured and theory-informed model that integrates pedagogical and technological elements. The model offers a practical framework for designing learning environments that support higher-order thinking and reflective learning processes in pre-service teacher education.