computational thinking

In the digital era, teaching programming and artificial intelligence to children and adolescents has become an essential need for developing cognitive skills, problem-solving, and computational thinking. This study aims to examine the challenges and opportunities of teaching these skills in educational systems.

This research is descriptive-analytical and was conducted using a survey method. The statistical population consisted of 300 students, teachers, parents, and educational managers, selected through stratified random sampling. The research instrument was a standardized questionnaire, validated by experts, with a Cronbach’s alpha reliability coefficient of 0.84. Data were analyzed using SPSS software and Python.

The findings indicated that the main challenges in teaching programming and Artificial Intelligence (AI) include a lack of standardized educational resources, a shortage of specialized teachers, traditional teaching methods, and parental unawareness. Conversely, modern educational methods such as gamification, project-based learning, and interactive technologies effectively enhance motivation and comprehension of complex concepts. Additionally, AI-based personalized feedback plays a crucial role in improving learning quality.

This study emphasizes the importance of adopting innovative teaching methods, training specialized teachers, developing age-appropriate educational resources, and integrating interactive technologies into educational environments. The findings provide valuable insights for educational policymakers, teachers, and parents to improve teaching strategies. It is recommended that schools utilize emerging technologies such as virtual reality and artificial intelligence to enhance students’ learning experiences.

The rapid advancement of artificial intelligence (AI) has introduced new dimensions to the digital divide, creating an AI divide that affects access, benefits, and opportunities across different regions and socioeconomic groups. Artificial intelligence (AI) literacy is increasingly vital in today's world as it becomes an integral part of daily life and the workforce. It involves understanding how AI works, its capabilities, limitations, and ethical implications. AI literacy empowers individuals to critically evaluate AI outputs, ensuring informed decision-making and responsible use of AI technologies. In education, AI literacy is crucial for educators and students to leverage AI for improved learning outcomes and educational management. It also opens up new job opportunities and enhances professional skills, making it a differentiating skill for future leaders. Moreover, with the AI Act mandating AI literacy for organizations using AI systems, it is essential for compliance and ethical AI deployment. By developing AI literacy, individuals can navigate an AI-infused world effectively, ensuring they remain informed consumers and critical thinkers. Overall, AI literacy is not just about technical knowledge but also about understanding AI's societal and ethical implications, making it indispensable for personal and professional growth in the digital age.  This study investigates the relationship between the digital divide and AI literacy, with a focus on the mediating roles of computational thinking and cognitive absorption among student-teachers. In today's technology-driven world, computational thinking (CT) and cognitive absorption are crucial skills that enhance an individual's ability to navigate complex digital environments effectively. Computational thinking involves problem-solving skills such as decomposition, pattern recognition, and algorithmic thinking, which are foundational for understanding and applying A) concepts. Cognitive absorption, on the other hand, refers to the degree to which an individual becomes engaged and immersed in a technological environment, which can significantly influence their learning and interaction with AI systems. CT skills are essential for breaking down complex problems into manageable parts, analyzing data, and developing solutions. This skillset is vital for educators and students alike, as it enhances their ability to integrate AI tools into educational settings. Studies have shown that computational thinking is a significant predictor of AI literacy, meaning that individuals with strong CT skills tend to have better understanding and application of AI concepts. In an era where AI and machine learning are rapidly evolving, CT skills enable individuals to adapt quickly to new technologies and innovations.

This study employed a descriptive-correlational method, collecting data from a sample of 946 student-teachers at the Farhangian University of Zanjan using various scales (AI literacy skill, Computational Thinking, Cognitive absorption, and Information and Communication Technology Access Scale) and the census method. Data analysis was conducted using PLS-4 and SPSS-27 software.

The study reveals that computational thinking is a significant factor in enhancing AI literacy. It indicated the pivotal role of CT in enhancing artificial intelligence AI literacy. Computational thinking, a problem-solving approach that involves decomposition, pattern recognition, and algorithmic thinking, is crucial for understanding and effectively using AI technologies. By fostering CT skills, individuals can better comprehend AI concepts, critically evaluate AI outputs, and apply AI solutions to real-world problems. It also shows that access to information and communication technologies (ICTs) facilitates better use of AI by individuals. Furthermore, having more motivation and skills to use AI technologies leads to a more positive experience. Notably, the findings indicate an indirect relationship between the digital divide and AI literacy, mediated by computational thinking and cognitive absorption. This suggests that these mediating factors play an additive role in bridging the gap between the digital divide and AI literacy. The results highlight the importance of promoting computational thinking and cognitive absorption to improve AI literacy among student-teachers. They also underscore the need for equitable access to ICTs to ensure that individuals can effectively engage with AI technologies. Ensuring equitable access to information and communication technologies (ICTs) is crucial for individuals to effectively engage with artificial intelligence (AI) technologies. This access is not merely a technological issue but also deeply intertwined with socio-economic factors, such as income and geographic location, which can exacerbate the digital divide.

 The digital divide refers to the unequal access to digital technologies, including computers, smartphones, and the internet, among different demographic groups and regions. This gap is not just about physical access but also encompasses differences in digital literacy and the ability to effectively use these technologies. The digital divide is multifaceted, involving disparities in access (first-level digital divide) and usage (second-level digital divide) of ICTs. It affects various socioeconomic groups within countries and between developed and developing nations, exacerbating existing social inequalities and creating a persistent information gap between those with access ("haves") and those without ("have-nots"). Factors contributing to the digital divide include affordability, availability of technology, geographic location, and education level. For instance, rural areas often have limited internet access compared to urban regions, while lower-income households may struggle to afford modern devices and internet services. Addressing the digital divide requires comprehensive strategies, such as investing in infrastructure, promoting digital literacy, and implementing policies to ensure equitable access to technology. Closing this gap is crucial for ensuring that all individuals can participate fully in the digital economy and benefit from its opportunities. This study contributes to understanding how educational interventions can address the AI divide by focusing on these mediating factors, ultimately enhancing digital equity and inclusivity in the educational sector. In conclusion, this study provides insights into how computational thinking and cognitive absorption mediate the digital divide and AI literacy relationship. It emphasizes the need for targeted educational strategies to enhance AI literacy and reduce the digital divide, ensuring that future educators are equipped to navigate and leverage AI technologies effectively. By addressing these challenges, educational institutions can foster a more inclusive and digitally literate community.

The purpose of this study was to identify and explain the features, capabilities, and components of computational thinking as one of the basic learning skills at the primary school level. This qualitative study used meta-analysis to identify the components of computational thinking. For this purpose, Persion keywords such as “thinking”, “thinking skills”, “computational thinking in primary school”, and “computational thinking in students”, together with their equivalents in English, were searched in MagIran, Sid, Civilica, ScienceDirect, Scopus databases, Institute of Electrical and Electronics Engineers from 1390 to 1399 and 2000 to 2021 in Persian and English sources, respectively. Data were collected using Sandelowski and Barroso’s seven-stage metasynthesis method. The sample was selected using acceptance and non-acceptance criteria and Critical Appraisal Skills Programme. The credibility of the results was checked using the researcher’s self-monitoring, peer review, and expert survey. Initially, 1643 articles were extracted, of which 1533 were screened based on the title, 35 based on the abstract, and 51 based on the content. Finally, 24 articles were selected as the study sample and analyzed. Based on the findings, the components of computational thinking can be classified into nineteen general categories. The results, furthermore, highlighted the importance of the following three basic categories in cultivating computational thinking in schools, especially during childhood and the early years of primary schooling: cognitive skills, communication and information skills (technology), and skills related to science, engineering, and mathematics. There was a high level of overlap and a positive relationship between the identified components and categories of computation thinking. In sum, it can be stated that computational thinking is an emerging problem-solving skill that should be taught and learned through technology and computer science in early childhood and primary schools.