Presenting an Artificial Intelligence Curriculum Model in Primary Education of the of Iran
Keywords:
Artificial intelligence curriculum model, primary educationAbstract
This research was conducted with the aim of presenting an artificial intelligence curriculum model in primary education within the Iran. The current study was applied in terms of its objective and utilized a mixed qualitative approach (classical grounded theory and the Delphi method). The study population comprised curriculum specialists in virtual education and Information and Communication Technology (ICT) nationwide during the 2024–2025 academic year. Purposive sampling was employed until theoretical saturation was achieved; consequently, to reach data sufficiency, interviews were conducted with 25 individuals. Interviews were utilized as the primary method for data collection. Data analysis was performed based on open coding, axial coding, and selective coding systems. To ensure the validity and reliability of the extracted concepts, the derived concepts were shared with the interviewees (member checking), and procedures to establish the dependability of the extracted data in alignment with the research objectives were applied. Overall, the results indicated that the components of the artificial intelligence curriculum model in primary education of the Islamic Republic of Iran consisted of ten dimensions: objectives (6 axial codes), content (4 axial codes), learning activities (4 axial codes), tools (6 axial codes), resources (6 axial codes), time scheduling (5 axial codes), learning environments (4 axial codes), evaluation (5 axial codes), grouping (4 axial codes), and rationale (5 axial codes). Experts’ opinions indicated that the proposed artificial intelligence curriculum model is founded on: skill-based and technology-driven objectives, forward-looking content and digital literacy, practical and innovative activities and tools, rich human and knowledge resources, a technology-driven and blended learning environment, and a multipurpose, forward-looking evaluation system. Components with lower Item-Level Content Validity Index (I-CVI) and Content Validity Ratio (CVR) scores (e.g., affective and attitudinal aspects, innovative instructional design, financial resources, and interests and aptitudes) require modification, revision, or reinforcement to ensure the curriculum is comprehensive and aligned with the future-oriented objectives of artificial intelligence.
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Copyright (c) 2025 Elnaz Mazaheri Foroushani, Zohreh Saadatmand, Mostafa Ghaderi (Author)
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