Identifying Factors Affecting the Use of Artificial Intelligence in Learning for Elementary School Students and Developing an Appropriate Qualitative Framework
Keywords:
Artificial intelligence, learning, personalized learning, elementary education, qualitative frameworkAbstract
The objective of this study was to identify key factors influencing the use of artificial intelligence in the learning of elementary school students and to develop a valid and comprehensive qualitative framework. This applied study employed a qualitative exploratory design based on the systematic grounded theory approach. Participants included 16 experts in artificial intelligence, learning sciences, and educational management, selected through snowball sampling until theoretical saturation was reached. Data were collected using semi-structured interviews and analyzed through open, axial, and selective coding using MAXQDA software. Credibility and reliability were ensured through retesting, inter-coder agreement, and validation using new interviewees. The analysis resulted in 106 open codes and 19 axial codes organized within six dimensions of the paradigm model. The core phenomenon consisted of learning enrichment and individualized learning. Causal factors included technical infrastructure, smart support, and technological readiness in families. Contextual factors were identified as intelligent learning management, AI-based learning content, and teachers’ competencies. Intervening conditions included ethics and information security, along with educational technology. Strategies encompassed personalized content, intelligent feedback, smart assessment, content quality enhancement, and learning assistance. Consequences included deep learning, improved teacher efficiency, high-quality assessment, and educational innovation. The study demonstrates that adopting artificial intelligence in elementary education can meaningfully enhance learning quality by providing personalized content, real-time feedback, and flexible learning support. The proposed framework offers practical guidance for educators and policymakers seeking to integrate AI tools effectively to promote deep learning and improve instructional processes.
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Copyright (c) 2025 Akram Heidarian (Author); Hamid Shafizadeh (Corresponding author); Narges Shariatmadari (Author)
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