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Volume 18, Issue 2 (2025)
JMED 2025, 18(2): 1-16 |
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Mazlomi A, Momeni Mahmouei H, Ajam A A. Presenting a comprehensive pattern of artificial intelligence in the students' education process: Meta-synthesis approach based on the Erwin Model. JMED 2025; 18 (2) :1-16
URL: http://edujournal.zums.ac.ir/article-1-2341-en.html
URL: http://edujournal.zums.ac.ir/article-1-2341-en.html
1- Department of Educational Sciences, Islamic Azad University, Torbat Heydariyeh Branch,Torbat Heydariyeh, Iran. , alimazlom272@yahoo.com
2- Department of Educational Sciences, Islamic Azad University, Torbat Heydariyeh Branch, Torbat Heydariyeh, Iran
3- Department of Educational Sciences, Payame Noor University, Tehran, Iran.
2- Department of Educational Sciences, Islamic Azad University, Torbat Heydariyeh Branch, Torbat Heydariyeh, Iran
3- Department of Educational Sciences, Payame Noor University, Tehran, Iran.
Abstract: (183 Views)
Background & Objective: Artificial Intelligence (AI) is transforming education by taking on tasks once reserved for humans, resulting in a revolution in the field. This study provides a comprehensive analysis of the components and indicators of AI in the students' education process.
Materials & Methods: This study employed a qualitative meta-synthesis approach, following the model proposed by Erwin et al. (2011). A total of 244 articles were included, consisting of scientific papers published in reputable journals such as PubMed and others, covering the years 1393 to 1403 AD (2014 to 2023) and focusing on the role of AI in the educational process of students. A purposive sampling method was used to select 32 qualitative studies from these 244 articles. Data were collected through qualitative analysis of the documents. Scott's (2012) coding method was used to ensure the coding process's reliability, as MacHie (2012) recommended. The inter-rater reliability was calculated at 85.4%.
Results: Based on the analysis of the selected qualitative studies, the components of artificial intelligence in the students' education process were categorized into six major dimensions and 21 sub-components. The identified dimensions include: (1) Knowledge of AI Elements (e.g., educational and learning approaches, program structure, effective learning strategies, educational impact, and learning perception), (2) Planning Knowledge (e.g., curriculum design and educational planning), (3) Humanistic Knowledge (e.g., emotional literacy, motivation, creativity, and interpersonal interaction), (4) Contextual Knowledge (e.g., understanding of culture, social context, and professional skills), (5) Meta-Knowledge (e.g., perceptual insight, experience-based learning, creative cognition, and technological proficiency), and (6) Attitudinal Knowledge (e.g., learners' positive or negative attitudes toward AI in education).
Conclusion: Based on the findings, effectively applying AI in the educational process requires attention to several key elements. These include AI literacy, planning knowledge, humanistic knowledge, contextual knowledge, meta-knowledge, and attitudinal knowledge for learners across all fields.
Materials & Methods: This study employed a qualitative meta-synthesis approach, following the model proposed by Erwin et al. (2011). A total of 244 articles were included, consisting of scientific papers published in reputable journals such as PubMed and others, covering the years 1393 to 1403 AD (2014 to 2023) and focusing on the role of AI in the educational process of students. A purposive sampling method was used to select 32 qualitative studies from these 244 articles. Data were collected through qualitative analysis of the documents. Scott's (2012) coding method was used to ensure the coding process's reliability, as MacHie (2012) recommended. The inter-rater reliability was calculated at 85.4%.
Results: Based on the analysis of the selected qualitative studies, the components of artificial intelligence in the students' education process were categorized into six major dimensions and 21 sub-components. The identified dimensions include: (1) Knowledge of AI Elements (e.g., educational and learning approaches, program structure, effective learning strategies, educational impact, and learning perception), (2) Planning Knowledge (e.g., curriculum design and educational planning), (3) Humanistic Knowledge (e.g., emotional literacy, motivation, creativity, and interpersonal interaction), (4) Contextual Knowledge (e.g., understanding of culture, social context, and professional skills), (5) Meta-Knowledge (e.g., perceptual insight, experience-based learning, creative cognition, and technological proficiency), and (6) Attitudinal Knowledge (e.g., learners' positive or negative attitudes toward AI in education).
Conclusion: Based on the findings, effectively applying AI in the educational process requires attention to several key elements. These include AI literacy, planning knowledge, humanistic knowledge, contextual knowledge, meta-knowledge, and attitudinal knowledge for learners across all fields.
Article Type : Orginal Research |
Subject:
Education
Received: 2024/11/27 | Accepted: 2025/05/21 | Published: 2025/07/13
Received: 2024/11/27 | Accepted: 2025/05/21 | Published: 2025/07/13
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