Design and Validation of a Future School Model Based on the Role of Artificial Intelligence in Elementary Schools

Abstract

Purpose: The objective of this study was to design and validate a future school model based on the role of artificial intelligence (AI) in elementary schools. Methodology: This research employed a qualitative methodology, conducted in two phases: design and validation. In the design phase, grounded theory and snowball sampling were used to select 18 experts in AI and education. In the validation phase, 20 experts, including university professors and senior education managers from Golestan Province, were selected through purposive sampling. Data analysis in the qualitative phase involved open, axial, and selective coding, while the Delphi method was used for validation over three stages. Findings: The designed model included 12 main categories and 24 subcategories, classified into five key components: causal conditions (cultural readiness, AI infrastructure, and family-centered interactions with AI), contextual conditions (AI-based virtual networks and social interactions), intervening conditions (technical and institutional challenges), strategies (AI-based evaluation and creative learning), and outcomes (improvement in the quality and speed of education, increased educational equity, and enhanced academic motivation). The results indicated that AI can significantly improve learning quality and educational equity. Conclusion: This study demonstrated that AI has strong potential to enhance educational processes and promote equity in elementary schools. However, the successful implementation of AI requires adequate infrastructure, cultural readiness, and proper management. The research provided solutions for addressing institutional and technical challenges in the path toward smart schooling.