Your new experience awaits. Try the new design now and help us make it even better

SYSTEMATIC REVIEW article

Front. Educ., 25 August 2025

Sec. Digital Education

Volume 10 - 2025 | https://doi.org/10.3389/feduc.2025.1535135

Integrating computational thinking in children aged 3 to 6: challenges and opportunities in early childhood education

  • Universidad de La Sabana, Facultad de educación, Chía, Colombia

Computational thinking (CT) has emerged as a crucial skill in 21st-century education. Although most research has focused on education levels beyond primary school, this article reviews the impact of its integration in early childhood education, specifically for children aged 3–6 years old. Through a systematic review of 84 studies published in Scopus and Web of Science between 2013 and 2023, pedagogical strategies and technological tools used to develop CT in early childhood are explored. The findings reveal that while CT fosters key cognitive and motor skills, the lack of appropriate materials and teacher training hinders effective implementation. The article highlights the need for continuous teacher training programs and the comprehensive inclusion of CT in early childhood curricula. Furthermore, it identifies a lack of consistent assessment tools that measure the long-term effects of these interventions on child development.

Introduction

Computational thinking (CT) has become established as an essential competency in the contemporary digital society. Wing (2011) described it as “the new literacy of the twenty-first century,” highlighting its relevance in both educational and professional environments. In early childhood education, particularly between the ages of three and six, CT not only lays the foundation for logical reasoning and problem-solving but also fosters key dispositions such as curiosity, creativity, and perseverance (Bers, 2018).

However, the effective integration of CT into early childhood education faces several challenges. One of the most prominent is the prevailing tendency in educational strategies to prioritize the use of digital tools such as robots and apps while neglecting approaches more appropriate for young children’s development, such as unplugged or play-based activities (Brackmann et al., 2017; Akiba, 2022).

Another significant challenge is the lack of conceptual clarity in defining and evaluating CT outcomes. Many educational interventions do not adequately distinguish between cognitive skills, such as abstraction or sequencing, and learning dispositions, such as persistence or curiosity (Sun et al., 2021). This distinction is crucial, as both dimensions are essential to understanding the full impact of CT in early childhood education (Liu et al., 2023).

Furthermore, there is a disconnect between CT initiatives and the developmental characteristics of young children. Although digital tools can be valuable resources for exploration and creativity, their use must be mediated by pedagogical strategies that respond to children’s social, emotional, and cognitive needs (Limón, 2022). Otherwise, implementing tools such as robots or screens from a utilitarian perspective may lead to student disengagement and limit the development of critical competencies for digital citizenship (Akiba, 2022; Artecona et al., 2016).

In this context, it is necessary to reconsider how CT is introduced in early childhood education and to explore pedagogical approaches that integrate both technical and human dimensions. Understanding CT not merely as a set of analytical tools but as a pedagogical framework that supports exploration, autonomy, and engagement opens the path toward more inclusive and transformative educational practices (OECD, 2020; Bers, 2018).

The role of computational thinking in cognitive development

Diverse academic disciplines use computational thinking as a fundamental cognitive process for problem-solving, providing students with practical experience in addressing real-world challenges and encouraging engagement with computer science (Master et al., 2023). Computational thinking embodies a form of analytical reasoning that closely resembles mathematical reasoning (e.g., problem-solving), engineering reasoning (process design and assessment), and scientific reasoning (systematic analysis).

Wing (2006) asserts that computational thinking “is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability.” (p. 33). The International Society for Technology in Education (ISTE) and the Computer Science Teachers Association (CSTA) have articulated an operational definition of computational thinking as a problem-solving methodology encompassing algorithmic problem formulation, logical data organization, abstraction as a representational mechanism, automation, resource and time efficiency, and solution generalization. Furthermore, elements such as confidence, persistence, tolerance for uncertainty, and communication are emphasized (Gerosa et al., 2022).

Consequently, computational thinking extends beyond just technical programming abilities; it is regarded as a cross-disciplinary cognitive process that may be incorporated into diverse fields of education. This strategy fosters the development of coding skills in early childhood, specifically in children aged 3–6, by employing strategies that enhance logical and sequential thinking. Prior studies indicate that preschoolers may acquire fundamental programming principles through the use of visual tools and interactive resources, fostering the development of algorithmic thinking in a natural and joyful manner (Bers, 2018).

Computational thinking enhances functional skills in domains such as literacy and mathematics (Clements and Sarama, 2018). The incorporation of computational thinking in early childhood education has demonstrated efficacy in enhancing numerical comprehension and mathematical problem-solving via logical sequences and repetitive patterns, which are essential concepts in programming and mathematics (Relkin et al., 2020).

From this viewpoint, computational thinking represents a method for systematically assessing, formulating, and resolving problems. This approach involves deconstructing big issues into smaller components, recognizing commonalities, formulating sequential solutions, and employing fundamental ideas such as assessment, pattern recognition, abstraction, and algorithm development. The early development of thinking, crucial in the 21st century, is critical for fostering pupils’ cognitive progress.

In addition to its recognition as a cognitive process that underpins analytical reasoning and problem-solving, computational thinking is increasingly conceptualized as a pedagogical framework that supports the design of developmentally appropriate, inquiry-based, and engaging learning environments in early childhood (Bers, 2018; Clarke-Midura et al., 2023). This dual perspective enhances its relevance in educational contexts, allowing it to function not only as a transferable cognitive competency but also as a structuring element for curricular innovation and interdisciplinary learning.

Computational thinking in early childhood education

Early childhood computational thinking is a crucial cognitive process aimed at cultivating problem-solving abilities and digital capabilities from a young age. Computational thinking transcends its role as a programming tool; it is a cognitive process that facilitates problem structuring, idea organization, and strategy development for resolution. The incorporation of digital solutions in early childhood education promotes children’s ability to create and implement such solutions, enhancing creativity and knowledge construction, thereby equipping them to traverse a digitized society (Pugnali et al., 2017; Relkin et al., 2020). This method not only includes programming education but also cultivates the capacity to deconstruct intricate problems into manageable components, recognize commonalities, and formulate sequential solutions, thereby fostering analytical and algorithmic reasoning from a young age (Kanaki and Kalogiannakis, 2022).

Clarke-Midura et al. (2023) emphasize the groundbreaking contributions of Papert in the 1970s and 1980s, who utilized the “cybernetic” turtle within the LOGO environment as an educational tool to cultivate mathematical reasoning in youngsters through engagement with “thinking objects.” Papert developed these items not merely to teach mathematics but also to involve children in essential elements of computational thinking, such as sequencing and debugging. This methodology has profoundly impacted contemporary research on imparting computational thinking in early childhood education.

Bers presents seven significant concepts for advancing early childhood computer education, derived from the Papert foundations: algorithms, modularity, control structures, representation, hardware and software, design processes, and debugging (Sullivan and Umashi Bers, 2018, p. 17). These concepts not only enhance technical abilities but also solidify computational thinking as a cognitive process that fosters the development of logical reasoning and systematic problem-solving from an early age.

Understanding that computational thinking in early infancy extends beyond computer science is crucial, since it fosters numerous abilities vital for all citizens (Caballero-González and García-Valcárcel, 2020). Education equips children for future technological applications such as coding and robotics, hence fostering broader and more equitable access to the digital skills essential for the future (Lu and Fletcher, 2009, as quoted by Bezuidenhout, 2021).

Developed nations regard computational thinking as the foundation of the emerging technology society, whilst poor nations perceive it as the optimal means to bridge educational disparities Basogain et al. (2016). Educators are progressively acknowledging the significance of this skill in education, while research remains insufficient about the methods to teach and cultivate computational thinking in young children outside conventional systems (Li and Yang, 2023).

Previous literature on early childhood computational thinking has predominantly examined the integration and impact of computational thinking within STEAM and STEM disciplines, alongside the utilization of technology as a programming instrument to augment 21st-century skills via robotics (Hu et al., 2024; Hu, 2024; Martins et al., 2023; Rich et al., 2024; Silva et al., 2023; Su and Yang, 2023; Zeng et al., 2023; Zhang and Crawford, 2023). This review focuses on the development of learning through computational thinking in children aged 3–6 years. For this purpose, the following general question was posed: what dimensions or concepts of learning are affected by CT? In this context, three specific questions guide the results presented: (1) What are the primary educational needs? (RQ1) (2) How does computational thinking affect the overall learning progress and cognitive development of early childhood education students? (RQ2) (3) What specific tools and practices have been employed to promote computational thinking in early childhood education, and what criteria are used to assess their effectiveness? (RQ3).

These questions seek to go beyond its function as a tool for introducing programming concepts or developing specific cognitive skills. Computational thinking can also be understood as a pedagogical framework that organizes young children’s interaction with learning tasks. When intentionally integrated into the curriculum, computational thinking encourages educators to design activities that promote logical reasoning, creativity, collaboration, and iterative problem-solving. This broader perspective aligns with constructionist approaches (Papert, 1980; Bers, 2018), which position computational thinking not only as a content to be taught, but as a fundamental perspective for structuring early learning experiences.

Methodology

Research on the development of computational thinking in early childhood has gained traction in the academic community, so it is important to review this field’s application to get a more in-depth understanding of how early childhood uses and appropriates computer thought.

This systematic review aims to track the development of computational thinking research in early childhood education. Recognizing this as the foundation of emotional and cognitive well-being across the lifespan, as well as one of the most advantageous investments a nation can undertake, given its promotion of holistic development, gender equality, and social cohesion (UNESCO, 2024).

While numerous studies have confirmed the efficacy of computational thinking in education, with some even extending its application to early childhood education, the obstacles, or strategies for incorporating computational thinking into preschool curricula remain unclear. We highlight the need to reveal how computational thinking achieves learning and how to integrate it into the early childhood curriculum.

This review has specific objectives. (1) Identify educational needs that have enabled the use of computational thinking in preschool education. (2) Recognize the importance of computational thinking within the learning process of preschool students. (3) Examine the tools and strategies used in preschool education to develop computational thinking. The findings will enable the academic community to deepen the teaching of early childhood computatnal thinking within the scope of learning achievements, providing a more analytical look at the use of tools used to develop computational thought in young children.

Method

This systematic review used the guidelines set out in the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) (see Figure 1). The recognized method establishes each step, leading to the development of a review protocol for the literature search process, eligibility criteria, and data extraction.

FIGURE 1
World map highlighting the number of articles per country using different colors. The legend indicates categories: less than 2, 2-4, 4-6, 6-8, and more than 8 articles, each represented by distinct colors. Notable countries with high article counts are indicated by the lightest color.

Figure 1. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). Own creation.

Data search process

In February and March 2024, we selected two multidisciplinary and high-impact academic databases Scopus and Web of Science (WoS) to carry out the literature search for this systematic review. These platforms were chosen for their comprehensive indexing of peer-reviewed journals in the fields of education, technology, and early childhood studies. Subsequently, a search string was constructed using a combination of controlled vocabulary and free-text terms to capture literature related to computational thinking, early childhood, preschool education, and kindergarten. The search terms were iteratively refined to ensure alignment with the study’s scope and objectives. Publications from the year 2024 were excluded to maintain consistency, as the searches were conducted prior to the completion of that publication year.

We combine several terms to refine the search and obtain more precise results. We used the following search equations: Scopus (TITLE-ABS-KEY (“computational thinking”) OR TITLE-ABS-KEY (“robot”) OR TITLE-ABS-KEY (“coding”) OR TITLE-ABS-KEY (“robotics”) OR TITLE-ABS-KEY (“programming”) AND TITLE-ABS-KEY (“early childhood”) OR TITLE-ABS-KEY (“young child*”) OR TITLE-ABS-KEY (“preschool*”) OR TITLE-ABS-KEY (“kindergarten*”) OR TITLE-ABS-KEY (“pre-k*”) OR TITLE-ABS-KEY (“childcare”) OR TITLE-ABS-KEY (“child care”) OR TITLE-ABS-KEY (“day care”)) WoS (“computational thinking” OR“robot” OR “coding” OR “robotics” OR “programming”) AND (“early childhood”OR “young child*” OR “preschool*” OR “kindergarten*” OR “pre-k*” OR “childcare” OR “child care” OR “day care”). In March 2024, researchers analyzed and accessed peer-reviewed articles published between 2013 and 2023.

The search string was designed to balance sensitivity and specificity, ensuring the inclusion of a comprehensive set of studies while minimizing irrelevant results. The terms “computational thinking,” “coding,” “robotics,” “robot,” and “programming” were selected to capture both the conceptual dimensions of Computational Thinking (CT) and its most frequent pedagogical applications in early childhood contexts. These terms are commonly used in the literature and have been employed in previous systematic reviews addressing similar topics. At the same time, the inclusion of population-specific terms such as “early childhood,” “young child,” “preschool,” “kindergarten,” “pre-k,” “child care,” and “day care” made it possible to identify studies explicitly focused on learners aged 3–6.

Eligibility criteria

Figure 1 displays the results of the PRISMA analysis, which indicated that a total of 10,196 articles were searched. A total of 9,620 papers were eliminated by automated filtering based on criteria such as publication year 2013–2023, topic (e.g., unrelated to education or computational thinking), keywords, and publication stage.

During the eligibility assessment phase, a total of 72 studies were excluded for various reasons, including lack of alignment with the school cycle or age (n = 28), absence of relation to computational thinking (n = 18), being systematic reviews (n = 7), focusing on teacher training in specific tools (n = 16), and duplicate records in the abstracts (n = 3) (see Table 1).

TABLE 1
www.frontiersin.org

Table 1. Inclusion and exclusion criteria for articles.

Data extraction

To ensure the rigor and reliability of the data extraction and coding process, two researchers independently coded all the selected studies using a structured coding framework that included the following analytical dimensions: educational needs, learning dimensions affected by computational thinking, tools and strategies employed, and methodological approaches.

The coding framework was developed iteratively based on the objectives of the review and theoretical references related to computational thinking in early childhood education. Discrepancies between the two independent coders were systematically discussed in consensus meetings. In cases where agreement could not be immediately achieved, a third researcher acted as an arbitrator to finalize the coding decisions.

This process of double independent coding followed by consensus and arbitration ensured the reliability of the classification. A detailed description of the coding categories, subcategories, and the criteria supporting their construction (see Tables 1, 2).

TABLE 2
www.frontiersin.org

Table 2. Summary of the 84 articles included in the systematic review.

This rigorous process contributed to increasing the credibility, trustworthiness, and replicability of the systematic review.

Data analysis procedure

Computational Thinking has emerged as a critical area of study and has been widely explored across diverse educational contexts. For this review, a total of 84 relevant studies published between 2013 and 2023 were identified. This body of literature is considered sufficiently comprehensive to provide a thorough overview of the implementation and application of CT in early childhood education. These studies were systematically examined to extract and categorize the most salient elements.

The analytical process employed a coding strategy grounded in a predefined framework encompassing four key analytical categories: (1) the identified problem or challenge, (2) the educational needs addressed, (3) the tools and strategies implemented, and (4) the methodological design adopted in each study (see Tables 1, 3).

TABLE 3
www.frontiersin.org

Table 3. Methodologies pertinent to study in computational thinking.

Each article was manually reviewed and coded using a structured coding matrix, which was developed iteratively and validated by the three authors to ensure inter-coder reliability. This process enabled the classification of recurring themes and patterns across the selected studies. Consistency in the coding procedure was achieved through cross-validation and double-checking of entries, allowing for a robust synthesis of findings. The adopted analytical approach provided a nuanced understanding of the pedagogical, technological, and contextual dimensions shaping the use of CT in early childhood education.

Results

The results presented in this section emerge from a comprehensive examination of the 84 texts selected following the application of the previously outlined inclusion and exclusion criteria. These studies facilitate the identification of the demands and effects that computational thinking has exerted on the social and cognitive development of children within early childhood education settings. Furthermore, given that the integration of computational thinking in early childhood education has been the subject of numerous investigations aimed at elucidating its effects, the initial focus will be an analysis of publication trends during the review period. This analysis will illustrate the increasing scholarly interest in this area, alongside the geographical distribution of the conducted studies, thereby providing a more nuanced understanding of the review’s findings.

Subsequently, the findings of the systematic review are presented, structured according to the three research questions (RQ1, RQ2, RQ3) formulated to guide this study. A meticulous examination of the selected studies allowed for the identification of the educational needs that underpinned the integration of computational thinking (RQ1), the cognitive and learning outcomes associated with its implementation (RQ2), and the specific tools, strategies, and evaluation criteria employed (RQ3).

These papers illustrate how CT enhances foundational skills in early childhood including problem solving, abstraction, and logical sequencing while shaping social and cognitive development (RQ2). As shown in Table 2, the results are organized into three key dimensions: (1) the educational demands driving CT integration (RQ1), (2) the pedagogical tools and tactics employed in classrooms (RQ3), and (3) the observed learning outcomes (RQ2). This structure provides a grounded perspective on the influence of CT, highlighting both its potential for curricular incorporation and the challenges identified in different contexts.

Descriptive statistical analysis of the selected studies

Publication trends

The systematic analysis of 84 studies reveals exponential growth in computational thinking (CT) research for early childhood education, directly informing our understanding of evolving pedagogical needs (RQ1) and implementation strategies (RQ3). As Figure 2 demonstrates, after limited output during 2013–2018 (1–3 articles/year), scholarly interest surged from 2019 (10 articles) onward, peaking in 2022–2023 (21 and 22 articles respectively). This trajectory mirrors increasing attention to both cognitive impacts (RQ2) and technological integration (RQ3) in early CT education.

FIGURE 2
Line chart and table displaying the number of articles from 2013 to 2023. The chart shows a gradual rise from 2013, with notable increases in 2019, 2021, and a peak at 22 articles in 2023. The table lists each year with corresponding article counts, confirming the same trend.

Figure 2. Total number of articles by year. Own creation.

Geographic distribution of research output

Figure 3 evidences crucial geographic disparities in contextualizing both educational needs (RQ1) and applicability of findings (RQ2 and RQ3). The United States (18) and Spain (17) account for 40% of the publications, while Germany, Australia, Brazil and China account for another 47%. The remaining 13% come from countries in the early stages of research, such as Costa Rica, India and Uruguay, highlighting the need to adapt strategies to diverse contexts (RQ1) and develop accessible tools (RQ3). This uneven distribution poses challenges for generalizing results on cognitive development (RQ2) and suggests opportunities for comparative research.

FIGURE 3
ā€œFlowchart titled ā€œIdentification of studies via databases and registers.ā€ It shows three phases: Identification, Screening, and Inclusion. 1. Identification: - Records identified: 2 from databases, 10,196 from registers. - Records removed before screening: 6,079 by automatic criteria, 3,257 by open access, 259 by keywords, publication stage criteria applied. 2. Screening: - Records screened: 587, with 415 excluded by title. - Reports sought: 172, with 16 duplicates. - Reports assessed for eligibility: 156. - Reports excluded for reasons like age/school cycle, non-computational thinking, systematic reviews, training specificity, and duplicates. 3. Included: - Reports of included studies: 84.ā€

Figure 3. Heat map. Own creation.

Concepts related to computational thinking

Computational thinking (CT) involves core computer science principles: breaking down problems, recognizing patterns, abstracting relevant information, and creating algorithms to solve problems (Wing, 2006). These are considered essential 21st-century skills, similar to reading, writing, and arithmetic (Wing, 2011). This review includes studies showing that CT skills can be developed in young children. Activities can help them systematically define problems, organize information, identify patterns, and plan solutions.

For example, Acosta et al. (2023); Angerami et al. (2022), and da Silva Ticon et al. (2022) demonstrate teaching CT using physical robotics and coding, allowing children to practice logical ordering and sequencing. Similarly, Gerosa et al. (2022) and Pérez-Suay et al. (2023) highlight how tasks like troubleshooting or modifying commands improve logical analysis and automated problem-solving. These approaches foster computational reasoning, adaptive thinking, and creativity.

The reviewed studies (see Table 2) show CT as a versatile skill that improves learning across subjects like computer science, mathematics, science, and language. Sullivan and Bers (2016) emphasize this versatility, a point supported by Pila et al. (2019) and Lavigne et al. (2023), whose studies show programming activities boosting mathematical reasoning through pattern exploration. Furthermore, Kim and Tscholl (2021) found that CT also enhances socio-emotional skills by encouraging collaboration and group problem-solving.

Overall, this review confirms that CT is widely integrated into early education. Traditional CT concepts have been successfully applied in classrooms with positive results. Table 4 summarizes the main concepts from the reviewed papers and how they relate to actual classroom practices.

TABLE 4
www.frontiersin.org

Table 4. Concepts related to computational thinking.

Identification of educational requirements for the use of computational thinking

The analysis focused on the specific educational needs in early childhood that prompted the use of computational thinking. Results show that 28.6% of the research used computational thinking to address educational objectives related to challenges in developing cognitive, social, and communication skills. Integrating computational thinking aims to enhance mathematical problem-solving, develop spatial skills and executive functions, facilitate language acquisition, and improve social interactions (see Table 5).

TABLE 5
www.frontiersin.org

Table 5. Articles on educational needs around the dimensions.

Conversely, studies recognized a need to improve the development of computational thinking skills in young children. To adapt programming and robotics activities to the cognitive and physical capabilities of preschool-aged children, 39% of the research focused on developing and implementing suitable and accessible tools, methodologies, and digital technologies for young children. The goal was for students to understand basic programming principles effectively and clearly, while also maintaining their motivation and interest (see Table 6). Most studies focused on using software tools such as robotic kits and programming applications, neglecting the opportunity to incorporate computational thinking from various viewpoints specific to the teacher and the students’ experiences.

TABLE 6
www.frontiersin.org

Table 6. Articles on educational needs around early childhood programming.

Research indicates that 17% of studies highlight several educational necessities for developing and assessing computational thinking in early childhood. These include: the absence or inadequacy of appropriate materials and technical equipment; fragmented teaching materials that hinder coherent robotics implementation; and a lack of formative and evaluative tools for assessing coding in child development. The authors agree on the importance of having methodologiest and tools that promote scientific literacy and computational thinking from an early age (see Table 7).

TABLE 7
www.frontiersin.org

Table 7. Articles on educational needs around the shortage of CT educational tools in the early childhood.

Moreover, the absence of confidence and education among educators obstructs the incorporation of computational thinking in early children, thereby generating a pressing requirement for purposefully crafted and simply available materials that may be seamlessly included into the preschool classroom. Merely 8% of the research demonstrate this scenario, wherein they admit that preschool teachers frequently lack the training and specialized skills to include computational thinking into the instruction. Why is it crucial to develop training frameworks and competencies for thorough teacher training? (see Table 8).

TABLE 8
www.frontiersin.org

Table 8. Articles on educational needs around the shortage of teacher training.

Methodologies pertinent to study in computational thinking

The analysis categorized the research based on their methodology as qualitative (33.3%), quantitative (28.5%), and mixed (35.7%). The predominant approaches employed are case studies, which aim to examine the specific circumstances and characteristics of various groups (see Table 3). In relation to data collection instruments, observation (see Table 3), and some combinations of instruments such as interviews and surveys are relevant (Pila et al., 2019; Sullivan and Bers, 2016; Unahalekhaka and Bers, 2022). Following that, Table 3 presents the recorded results pertaining to the employed approaches and instruments.

Computational thinking development artifacts

A synthesis of the reviewed literature highlights that robotic and digital tools are not simply devices used in isolation, but rather pedagogical mediators that facilitate meaningful interaction with the fundamental aspects of computational thinking in early childhood. When intentionally integrated, they foster cognitive, motor, linguistic, and socioemotional development through embodied and interactive learning experiences.

Tools such as Blue Bot, Bee Bot, and KIBO serve as tangible interfaces that translate abstract computational concepts into physically manipulable actions. Their programmable functions encourage sequencing, logical reasoning, spatial orientation, and collaborative problem-solving (Aranda et al., 2019; Fridberg et al., 2023; Jack et al., 2019; Pugnali et al., 2017). These tools have proven particularly effective in promoting engagement and immediate feedback, thereby reducing passive waiting times and maintaining children’s motivation (Bharatharaj et al., 2023; Odgaard, 2022). For example, KIBO encourages fine motor coordination and teamwork through block programming, while Blue Bot facilitates mathematical exploration and spatial perception.

Social robots further expand the pedagogical scope by supporting language development and social interaction. Their ability to simulate conversational exchanges has shown promise in enhancing second language acquisition and reinforcing sociobehavioral norms (Almousa and Alghowinem, 2023; Kim and Tscholl, 2021; Kory Westlund et al., 2017). In this context, Bee Bot, beyond being a simple programmable toy, becomes a flexible learning companion that adapts to diverse curricular content and promotes inclusive practices (Angeli and Georgiou, 2023; Caballero-González and García-Valcárcel, 2020; Caballero-González and Muñoz-Repiso, 2021).

Digital programs such as Scratch Jr. complement these tangible experiences by offering a visual programming environment that encourages symbolic thinking and creative expression without requiring literacy or typing skills. This approach allows students to experiment with algorithmic structures through storytelling and animation, fostering both computational fluency and narrative competence (Bers and Sullivan, 2019; Pugnali et al., 2017; Sullivan and Bers, 2013).

Across all studies, a common implication is that the educational potential of these tools lies not in their technological sophistication, but in how they are integrated into developmentally appropriate pedagogical practices. When implemented with intentional scaffolding, these artifacts contribute not only to the acquisition of programming skills, but also to broader competencies such as persistence, collaboration, abstraction, and creativity (Bezuidenhout, 2021; Clarke-Midura et al., 2021; Hollenstein et al., 2022; Lavigne et al., 2023; Yang et al., 2022).

Knowledge acquired via the use of computational thinking

Academic research emphasizes the positive impact of educational robotics on analytical skills, creativity, and problem-solving capabilities. (Álvarez-Herrero, 2021). In their recent publications, Papadakis (2022) and Чернобровкин et al., (2020) highlight the significance of cultivating digital abilities in children of preschool age. These skills encompass computational thinking and educational robotics, and involve proficiencies in sequencing, coding, and problem-solving.

The researchers in their study tackle the many obstacles associated with incorporating robots into the classroom curriculum, particularly in the context of early childhood education. (Su and Zhong, 2022).

Computational activities enhanced problem solving and mathematical reasoning (Somuncu and Aslan, 2022), while students innovative solutions to challenges demonstrated creativity development (Álvarez-Herrero, 2021; Kewalramani et al., 2020). Robotics and programming particularly fostered teamwork through collaborative problem solving (Clarke-Midura et al., 2021).

One of the goals of computational thinking in Early Childhood Education is to diversify learning activities, since research demonstrates the beneficial effects on cognitive, computational, and social development. Through the use of physical robots, children enhance their fine motor abilities and spatial conceptual understanding. (Montuori et al., 2023). The cultivation of computational thinking in education serves not only to enhance students’ academic achievements but also to facilitate their adjustment to a dynamic society that places growing importance on digital and technological proficiency.

Discussion

The analysis conducted on the 84 reviewed articles allowed for the identification of the aspects that motivate the integration of computational thinking in early education, the contributions that its integration makes to the different dimensions of child development, as well as the main challenges and difficulties encountered in its integration into early childhood education. Based on the findings obtained, several key points can be highlighted in relation to the objectives and research questions posed.

The examined research identified the varied educational needs that drive the incorporation of computational thinking into early childhood education, facilitating the development of both the child’s dimensions and age-appropriate learning. A significant reason cited in the literature is that computational thinking serves not only as a means for imparting technological skills but also enhances essential cognitive functions, including problem-solving, logical reasoning, creativity, and algorithmic thinking (Acosta et al., 2023; Angeli and Georgiou, 2023; Liu et al., 2023). While computational thinking is intended to enable students to organize ideas, devise solutions, and employ analytical processes across various contexts—thereby enhancing certain aspects of child development—many existing practices predominantly emphasize coding or specific computational thinking skills, rather than incorporating it comprehensively into the curriculum. Moreover, numerous research indicate that instruction in computational thinking enhances the development of mathematical and scientific competencies by promoting abstraction, pattern recognition, and sequential task organization (Kanaki and Kalogiannakis, 2022; Gerosa et al., 2022; Master et al., 2023). The incorporation of computational concepts from a young age fosters analytical thinking and informed decision-making, crucial for problem-solving in various fields (Bers, 2018), while also enhancing self-regulation and socio-emotional skills (Kim and Tscholl, 2021; Clarke-Midura et al., 2021). It is essential for early childhood educators to first comprehend the concept of computational thinking before devising the pedagogical strategies they intend to implement with children.

However, despite the promise of computational thinking in early childhood education, the analysis of papers identified obstacles that limit its effective application. The absence of targeted training for educators has been recognized as a primary concern. A significant number of early childhood educators have not undergone training in computational thinking and lack the pedagogical tools necessary for its integration into their daily practices (Leung, 2023; Monteiro et al., 2021; Otterborn et al., 2020). Their reluctance to integrate computational thinking in the classroom stems from the belief that it is solely a domain of computer science, highlighting the necessity for professional development programs that illustrate its relevance across various educational disciplines (Yadav et al., 2014; Li, 2014).

One of the critical challenges highlighted by the reviewed studies is the lack of a consistent curricular framework to guide the integration of computational thinking in early childhood education. This issue may stem from a conceptual ambiguity surrounding the nature of computational thinking whether it should be treated solely as a technical skill, a cognitive competency, or a broader pedagogical paradigm. This lack of definitional clarity can hinder the development of coherent educational policies and teacher training programs, leading to fragmented or superficial implementations. Addressing this gap requires a unified conceptual framing that positions computational thinking as both a cognitive process and an educational approach, enabling its meaningful incorporation across diverse early learning environments.

A major obstacle identified across the reviewed literature is the absence of coherent curricular frameworks for integrating computational thinking into early childhood education. Although international bodies such as the OECD and ISTE advocate for the early incorporation of CT, most countries have yet to develop concrete policies or curricular guidelines to support this integration (Su and Zhong, 2022; Pérez-Suay et al., 2023). This lack of structured direction leaves educators without a clear roadmap, resulting in considerable variability in how CT is interpreted and implemented at the classroom level. Without institutional guidance, CT often remains an optional enrichment activity rather than a fundamental component of early learning, limiting its scalability and long-term developmental impact.

The root of this curricular inconsistency may lie in the ongoing conceptual ambiguity surrounding the nature of computational thinking. As the reviewed studies suggest, CT is variably understood as a technical programming skill, a domain-general cognitive competency, or a comprehensive pedagogical approach. This definitional fragmentation undermines the development of unified policies and training programs, leading to disjointed implementation efforts and a lack of continuity across educational levels (Clarke-Midura et al., 2021; Leung, 2023). Addressing this challenge requires establishing a clear and integrative conceptual framework that positions CT simultaneously as a cognitive and pedagogical construct, thereby enabling more systematic and context-sensitive curricular integration.

The analysis of studies indicates that the use of computational thinking enhances the acquisition of fundamental abilities in children aged 3–6, hence contributing to their learning and cognitive development in early childhood education. The study demonstrates that computational thinking enhances the development of mathematics and linguistic abilities, while also promoting collaboration and collective decision-making. The review indicates that the influence of computational thinking is contingent upon the pedagogical approach employed, underscoring the necessity of establishing suitable procedures for its instruction in early childhood education.

The studies reviewed indicate that educational robotics and tools such as Scratch Jr. are the most widely used resources for fostering CT in early childhood, along with assessment methods. However, the data suggest that CT instruction should not rely solely on technology. In this regard, strategies such as physical games, storytelling, and manipulatives that work without electronic devices have been shown to be equally effective in teaching foundational concepts (e.g., sequencing, patterns) in a tangible and inclusive way, especially in low-resource settings (Brackmann et al., 2017; Bell et al., 2009).

The unevenness in CT implementation is striking: while some countries have integrated it into early education policies, others face barriers such as limited technology or teacher training. This calls for flexible and accessible methodologies adapted to diverse contexts. To ensure impact, future research should explore all types of tools that are applicable in rural or public schools.

In addition to the general trends identified, the systematic analysis of the 84 studies revealed significant contextual variations in CT implementation approaches. Geographically, the research corpus demonstrates pronounced disparities in scholarly representation, with studies predominantly originating from high-income nations (e.g., United States, Spain, Germany, and Australia), whereas research output from low- and middle-income regions (particularly Latin America, Sub-Saharan Africa, and Southeast Asia) remains disproportionately limited. This geographical imbalance highlights critical gaps in the literature and the necessity for expanded empirical investigations in currently neglected educational contexts, where distinct infrastructural conditions, pedagogical traditions, and sociocultural factors may substantially influence CT adoption.

Furthermore, the review identified recurring implementation challenges, particularly regarding educator capacity development, institutional commitment, and technological resource availability—constraints that appear most acute in resource-constrained educational systems. Methodologically, the studies exhibited considerable heterogeneity in research designs, encompassing qualitative case studies, semi-structured interviews, and quantitative digital assessments, reflecting the absence of established evaluation protocols for early childhood CT interventions. The pedagogical tools documented ranged from analog, unplugged activities to programmable robotics platforms, with implementation strategies frequently adapted to local resource availability and prevailing educational philosophies.

Conclusion

This review offers an analysis of the fundamental principles, educational requirements, approaches, tactics, and instruments which are frequently employed in the incorporation of computational thinking in early childhood education. Based on these categories, the identified research gaps provide an opportunity for professionals who are interested in computational thinking and its early educational impact on children.

The findings suggested that the application of computational thinking in preschool education has a beneficial impact on the cognitive and motor capacities of children. Moreover, there is a recommendation to the academic community to include computational thinking throughout the curricula of early childhood education.

The analysis has several constraints, including the exclusive incorporation of applied research articles, disregarding review documents, conference papers, and other relevant sources, as well as the limited availability of literature on teacher training in computational thinking for early children.

Despite the value of this systematic review, it is important to acknowledge some limitations that may have influenced the findings obtained. Firstly, the literature search was conducted exclusively in Scopus and Web of Science, which, while ensuring a high standard of quality in the selected studies, may also have excluded relevant research published in other specialized databases or in gray literature. The inclusion of other sources, such as Google Scholar or regional databases, could have provided a broader perspective on the integration of computational thinking in early education.

Secondly, the review focused solely on open-access articles, which, while facilitating the replicability of the study and access to information, also limits the inclusion of studies with a more specialized focus that are often restricted. This could have left out relevant research on the implementation of computational thinking in specific contexts or with larger samples.

Furthermore, most of the analyzed studies come from Europe and North America, which leaves a gap in research on the integration of computational thinking in regions such as Latin America, Africa, and Asia. The lack of studies in these contexts limits the generalization of the results and highlights the need to promote research in diverse educational contexts.

For future research and educational applications, it is essential to expand search sources to include additional databases and gray literature to comprehensively analyze CT in early childhood education. Studies should be expanded to underrepresented regions such as Latin America and Asia, identifying context-specific barriers and opportunities for implementation. Curriculum frameworks tailored to developmental stages should be designed to progressively integrate CT. Teacher training programs should also be prioritized, equipping educators with practical and adaptable strategies to ensure equitable access. Finally, robust evaluation tools are needed to objectively measure the impact of CT and interdisciplinary transfer, allowing for rigorous assessment of its effectiveness.

Building on these recommendations, it is crucial to take concrete steps to advance the integration of computational thinking in early childhood education. Future research should develop and validate flexible curricular frameworks that position CT as both a cognitive and pedagogical tool, adaptable to diverse global contexts. Longitudinal studies are needed to examine its sustained developmental impacts, particularly on social-emotional and creative domains. At the policy level, national curricula must prioritize CT through scalable models that align with early education goals. Equally, teacher education must incorporate CT-specific training grounded in early childhood pedagogies and inclusive of unplugged approaches. Coordinated action across research, practice, and policy will be essential to realizing the full potential of CT as a transformative element in early learning worldwide.

Limitations

One of the limitations of this study is the time period of the database search, which was conducted between February and March 2023. Consequently, more recent publications that could offer novel perspectives on the integration of computational thinking in early childhood education may have been excluded. This time limitation may slightly affect the comprehensiveness and timeliness of the findings. To address this limitation, future research should consider ongoing or updated searches, allowing for the incorporation of emerging studies, to ensure the most recent and relevant literature is systematically integrated into the analysis.

This review is that inter-coder reliability was ensured through a consensus-building process among independent coders, rather than through the calculation of statistical coefficients such as Cohen’s Kappa or Fleiss’ Kappa. While consensus and arbitration are widely accepted practices in qualitative systematic reviews to ensure coding reliability, the absence of a statistical measure may be considered a limitation for readers expecting quantitative validation. Nonetheless, the systematic approach adopted double independent coding, structured discussions, and third-party arbitration strengthens the credibility and consistency of the coding framework applied throughout the study.

Author contributions

GP: Data curation, Formal analysis, Investigation, Writing – original draft. OB: Formal analysis, Investigation, Supervision, Writing – review & editing. AV: Formal analysis, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This project was funded by the Doctoral Programme in Education and the General Direction of Research of the Universidad de La Sabana, Colombia (Colombian code: CTA-32-2017).

Acknowledgments

This article is part of a study conducted by Gineth Paola Perez Valdes entitled: “Designing a Curriculum Framework for Computational Thinking in Early Childhood”, which is part of his research in the Doctoral Program in Education at Universidad de La Sabana, Colombia.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that Generative AI was used in the creation of this manuscript. Quillbot was exclusively used to adjust the style of some texts within the document, to give it a higher academic quality.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Acosta, Y., Alsina, A., and Pincheira, N. (2023). Computational thinking and repetition patterns in early childhood education: Longitudinal analysis of representation and justification. Educ. Inform. Technol. 29, 7633–7658. doi: 10.1007/s10639-023-12051-6

Crossref Full Text | Google Scholar

Akiba, D. (2022). Computational thinking and coding for young children: A hybrid approach to link unplugged and plugged activities. Educ. Sci. 12:793. doi: 10.3390/educsci12110793

Crossref Full Text | Google Scholar

Almousa, O., and Alghowinem, S. (2023). Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children. User Model. User-Adapt. Interact. 33, 261–291. doi: 10.1007/s11257-022-09344-9

Crossref Full Text | Google Scholar

Alsina, Á (2023). Essential knowledge on processes, skills or mathematical competencies: Guidelines for implementing learning situations. EDMA 0–6 12, 65–108. doi: 10.24197/edmain.2.2023.65-108

Crossref Full Text | Google Scholar

Álvarez-Herrero, J. F. (2021). Design and validation of an instrument for the taxonomy of floor robots in early childhood education. Pixel-Bit 60, 59–76. doi: 10.12795/pixelbit.78475

Crossref Full Text | Google Scholar

Angeli, C., and Georgiou, K. (2023). Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots. Front. Educ. 7:757627. doi: 10.3389/feduc.2022.757627

Crossref Full Text | Google Scholar

Angerami, P. L., Raabe, A. L. A., and do Rosario, T. A. M. (2022). The lessons learned by children with the use of programmable toys. Dialogia 40, 1–20. doi: 10.5585/40.2022.21562

Crossref Full Text | Google Scholar

Aranda, M. R., Roca, M. E., and Martí, M. M. (2019). Didactical suitability in early childhood education: Mathematics with Blue-Bot robots. Edmetic 8, 150–168. doi: 10.21071/edmetic.v8i2.11589

PubMed Abstract | Crossref Full Text | Google Scholar

Artecona, F., Bonetti, E., Darino, C., Mello, F., Rosá, M., and Scópise, M. (2016). in Pensamiento Computacional: Un Aporte Para la Educación de hoy, ed. C. Roxlo (Buenos Aires: Fundación Movistar).

Google Scholar

Barman, L., and Kjällander, S. (2022). Playful and meaningful learning of programming: What does it take to integrate an app-based game promoting digital mathematics into early childhood education? Designs Learn. 14, 165–178. doi: 10.16993/dfl.203

Crossref Full Text | Google Scholar

Basogain, X., Olabe, M. A., Olabe, J. C., Ramírez, R., del Rosario, M., and Garcia, J. (2016). “PC-01: Introduction to computational thinking: Educational technology in primary and secondary education,” in Proceedings of the 2016 International Symposium on Computers in Education (SIIE), (Salamanca), 1–5. doi: 10.1109/SIIE.2016.7751816

Crossref Full Text | Google Scholar

Bell, T., Alexander, J., Freeman, I., and Grimley, M. (2009). Computer science unplugged: School students doing real computing without computers. N. Z. J. Appl. Comput. Inform. Technol. 13, 20–29.

Google Scholar

Berciano-Alcaraz, A., Salgado-Somoza, M., and Jiménez-Gestal, C. (2022). Computer literacy in early childhood education: Difficulties and benefits in a 3-year-old classroom. Rev. Electrón. Educ. 26, 270–290. doi: 10.15359/ree.26-2.15

Crossref Full Text | Google Scholar

Bers, M. U. (2018). Coding as a Playground: Programming and Computational Thinking in the Early Childhood Classroom. New York, NY: Routledge.

Google Scholar

Bers, M. U., González-González, C., and Armas-Torres, M. B. (2019). Coding as a playground: Promoting positive learning experiences in childhood classrooms. Comput. Educ. 138, 130–145. doi: 10.1016/j.compedu.2019.04.013

Crossref Full Text | Google Scholar

Bers, M. U., and Sullivan, A. (2019). Computer science education in early childhood: The case of ScratchJr. J. Inform. Technol. Educ. 18, 113–138. doi: 10.28945/4437

Crossref Full Text | Google Scholar

Betelin, V. B., Kushnirenko, A. G., Leonov, A. G., and Mashchenko, K. A. (2021). Basic programming concepts as explained for preschoolers. Int. J. Educ. Inform. Technol. 15, 245–255. doi: 10.46300/9109.2021.15.25

Crossref Full Text | Google Scholar

Bezuidenhout, H. S. (2021). An early grade science, technology, engineering and mathematics dialogue reading programme: The development of a conceptual framework. S. Afr. J. Childhood Educ. 11. doi: 10.4102/sajce.v11i1.1038

Crossref Full Text | Google Scholar

Bharatharaj, J., Pepperberg, I. M., Sasthan Kutty, S. K., Munisamy, A., and Krägeloh, C. (2023). Exploring the utility of robots as distractors during a delay-of-gratification task in preschool children. Front. Robot. AI 10:1001119. doi: 10.3389/frobt.2023.1001119

PubMed Abstract | Crossref Full Text | Google Scholar

Brackmann, C. P., Román-González, M., Robles, G., Moreno-León, J., Casali, A., and Barone, D. (2017). “Development of computational thinking skills through unplugged activities in primary school,” in Proceedings of the 12th Workshop on Primary and Secondary Computing Education, (New York, NY: ACM), 65–72. doi: 10.1145/3137065.3137069

Crossref Full Text | Google Scholar

Caballero-González, Y. A., and García-Valcárcel, A. (2020). Learning with robotics in primary education? A means of stimulating computational thinking. Educat. Knowl. Soc. 21, 1–15. doi: 10.14201/eks.22957

PubMed Abstract | Crossref Full Text | Google Scholar

Caballero-González, Y. A., and Muñoz-Repiso, A. G. V. (2019). Enhancing computational thinking skills in early childhood education: Learning experience through tangible and graphical interfaces. Rev. Latinoamericana Tecnol. Educ. 18, 133–149. doi: 10.17398/1695-288X.18.2.133

Crossref Full Text | Google Scholar

Caballero-González, Y. A., and Muñoz-Repiso, A. G. V. (2021). Robots in early childhood education: Learning to sequence actions using programmable robots. RIED 24, 77–94. doi: 10.5944/ried.24.1.27508

Crossref Full Text | Google Scholar

Campos, V. M., and Rodríguez Muñoz, F. J. (2023). Design and piloting of a proposal for intervention with educational robotics for the development of lexical relationships in early childhood education. Smart Learn. Environ. 10:6. doi: 10.1186/s40561-023-00226-0

PubMed Abstract | Crossref Full Text | Google Scholar

Clarke-Midura, J., Kozlowski, J. S., Shumway, J. F., and Lee, V. R. (2021). How young children engage in and shift between reference frames when playing with coding toys. Int. J. Child Comput. Interact. 28:100250. doi: 10.1016/j.ijcci.2021.100250

Crossref Full Text | Google Scholar

Clarke-Midura, J., Lee, V. R., Shumway, J. F., Silvis, D., Kozlowski, J. S., and Peterson, R. (2023). Designing formative assessments of early childhood computational thinking. Early Childhood Res. Q. 65, 68–80. doi: 10.1016/j.ecresq.2023.05.013

Crossref Full Text | Google Scholar

Clements, D., and Sarama, J. (2018). Myths of early math. Educ. Sci. 8:71. doi: 10.3390/educsci8020071

Crossref Full Text | Google Scholar

Critten, V., Hagon, H., and Messer, D. (2022). Can pre-school children learn programming and coding through guided play activities? A case study in computational thinking. Early Childhood Educ. J. 50, 969–981. doi: 10.1007/s10643-021-01236-8

Crossref Full Text | Google Scholar

da Silva Ticon, S. C., de Abreu Mól, A. C., and Legey, A. P. (2022). Atividades plugadas e desplugadas na educação infantil no desenvolvimento do pensamento computacional. Dialogia 40, e21751–e21751. doi: 10.5585/40.2022.21751

Crossref Full Text | Google Scholar

de Haas, M., Vogt, P., and Krahmer, E. (2020). The effects of feedback on children’s engagement and learning outcomes in robot-assisted second language learning. Front. Robot. AI 7:101. doi: 10.3389/frobt.2020.00101

PubMed Abstract | Crossref Full Text | Google Scholar

Demir-Lira, ÖE., Kanero, J., Oranç, C., Koşkulu, S., Franko, I., Göksun, T., et al. (2020). L2 vocabulary teaching by social robots: The role of gestures and on-screen cues as scaffolds. Front. Educ. 5:599636. doi: 10.3389/feduc.2020.599636

Crossref Full Text | Google Scholar

Dufranc, G., Sáez-López, J. M., Serio, Á, and Prats, M. A. (2020). Robotics and early-years STEM education: The botSTEM framework and activities. Eur. J. STEM Educ. 5:1. doi: 10.20897/ejsteme/7948

Crossref Full Text | Google Scholar

Fridberg, H., Redfors, A., Greca, I. M., and Terceño, E. M. G. (2023). Spanish and Swedish teachers’ perspective of teaching STEM and robotics in preschool: Results from the BotSTEM project. Int. J. Technol. Design Educ. 33, 1–21. doi: 10.1007/s10798-021-09717-y

Crossref Full Text | Google Scholar

García-Fuentes, O. (2022). La robótica educativa y el pensamiento computacional en la primera infancia y el hogar: Un estudio en la prensa digital. Digital Educ. Rev. 41, 124–139. doi: 10.1344/der.2022.41.124-139

Crossref Full Text | Google Scholar

Gerosa, A., Koleszar, V., Tejera, G., Gómez-Sena, L., and Carboni, A. (2022). Educational robotics intervention to foster computational thinking in preschoolers: Effects of children’s task engagement. Fronti. Psychol. 13:904761. doi: 10.3389/fpsyg.2022.904761

PubMed Abstract | Crossref Full Text | Google Scholar

Hollenstein, L., Thurnheer, S., and Vogt, F. (2022). Problem solving and digital transformation: Acquiring skills through pretend play in kindergarten. Educ. Sci. 12:92. doi: 10.3390/educsci12020092

Crossref Full Text | Google Scholar

Hu, L. (2024). Programming and 21st century skill development in K–12 schools: A multidimensional meta-analysis. J. Comput. Assist. Learn. 40, 610–636. doi: 10.1111/jcal.12904

Crossref Full Text | Google Scholar

Hu, X., Fang, Y., and Liang, Y. (2024). Roles and effect of digital technology on young children’s STEM education: A scoping review of empirical studies. Educ. Sci. 14:357. doi: 10.3390/educsci14040357

Crossref Full Text | Google Scholar

Jack, L. P., Khamis, N., Salimun, C., Nizam, D. M., Haslinda, Z., and Baharum, A. (2019). Learn programming framework for Malaysian preschoolers. Int. J. Adv. Trends Comput. Sci. Eng. 8, 431–436. doi: 10.30534/ijatcse/2019/6281.62019

Crossref Full Text | Google Scholar

Kanaki, K., and Kalogiannakis, M. (2022). Assessing algorithmic thinking skills in relation to age in early childhood STEM education. Educ. Sci. 12:380. doi: 10.3390/educsci12060380

Crossref Full Text | Google Scholar

Kewalramani, S., Palaiologou, I., and Dardanou, M. (2020). Children’s engineering design thinking processes: The magic of the robots and the power of blocks (electronics). Eur. J. Math. Sci. Technol. Educ. 16:em1830. doi: 10.29333/ejmste/113247

Crossref Full Text | Google Scholar

Kim, Y., Marx, S., Pham, H. V., and Nguyen, T. (2021). Designing for robot-mediated interaction among culturally and linguistically diverse children. Educ. Technol. Res. Dev. 69, 3233–3254. doi: 10.1007/s11423-021-10051-2

Crossref Full Text | Google Scholar

Kim, Y., and Tscholl, M. (2021). Young children’s embodied interactions with a social robot. Educ. Technol. Res. Dev. 69, 2059–2081. doi: 10.1007/s11423-021-09978-3

Crossref Full Text | Google Scholar

Kory Westlund, J. M., Dickens, L., Jeong, S., Harris, P. L., DeSteno, D., and Breazeal, C. (2017). Children use non-verbal cues to learn new words from robots as well as people. Int. J. Child Comput. Interact. 13, 1–9. doi: 10.1016/j.ijcci.2017.04.001

Crossref Full Text | Google Scholar

Kourti, Z., Michalakopoulos, C. A., Bagos, P. G., and Paraskevopoulou-Kollia, E. A. (2023). Computational thinking in preschool age: A case study in Greece. Educ. Sci. 13:157. doi: 10.3390/educsci13020157

Crossref Full Text | Google Scholar

Lavigne, H. J., Presser, A. L., Rosenfeld, D., Cuellar, L., Vidiksis, R., Ferguson, C., et al. (2023). Computational thinking with families: Studying an at-home media intervention to promote joint media engagement between preschoolers and their parents. Early Childhood Res. Q. 65, 102–114. doi: 10.1016/j.ecresq.2023.05.009

Crossref Full Text | Google Scholar

Leung, W. M. V. (2023). STEM education in early years: Challenges and opportunities in changing teachers’ pedagogical strategies. Educ. Sci. 13:490. doi: 10.3390/educsci13050490

Crossref Full Text | Google Scholar

Li, W., and Yang, W. (2023). Promoting children’s computational thinking: A quasi-experimental study of web-mediated parent education. J. Comput. Assist. Learn. 39, 1564–1575. doi: 10.1111/jcal.12818

Crossref Full Text | Google Scholar

Li, Y. (2014). “Research into the computational thinking for the teaching of computer science,” in Proceedings of the Frontiers in Education Conference (FIE), (Madrid: IEEE), doi: 10.1109/FIE.2014.7044465

Crossref Full Text | Google Scholar

Limón, C. (2022). Actualidad en primera infancia: Pensamiento computacional y digital en la infancia. Boletín Organ. Estados Iberoamericanos 21:11. doi: 10.5281/zenodo.7391581

Crossref Full Text | Google Scholar

Liu, X., Wang, X., Xu, K., and Hu, X. (2023). Effect of reverse engineering pedagogy on primary school students’ computational thinking skills in STEM learning activities. J. Intell. 11:36. doi: 10.3390/jintelligence11020036

PubMed Abstract | Crossref Full Text | Google Scholar

Lu, J. J., and Fletcher, G. H. L. (2009). “Thinking about computational thinking,” in Proceedings of the 40th SIGCSE Technical Symposium on Computer Science Education (SIGCSE 2009, Chattanooga TN, USA, March 4–7, 2009), eds S. Fitzgerald, M. Guzdial, G. Lewandowski, and S. A. Wolfman (New York, NY: Association for Computing Machinery, Inc.), 260–264.

Google Scholar

Manches, A., and Plowman, L. (2017). Computing education in children’s early years: A call for debate. Br. J. Educ. Technol. 48, 191–201. doi: 10.1111/bjet.12355

Crossref Full Text | Google Scholar

Martin, D. U., MacIntyre, M. I., Perry, C., Clift, G., Pedell, S., and Kaufman, J. (2020). Young children’s indiscriminate helping behavior toward a humanoid robot. Front. Psychol. 11:239. doi: 10.3389/fpsyg.2020.00239

PubMed Abstract | Crossref Full Text | Google Scholar

Martins, E. C., da Silva, L. G. Z., and Neris, V. P. D. A. (2023). Systematic mapping of computational thinking in preschool children. Int. J. Child Comput. Interact. 36:100566. doi: 10.1016/j.ijcci.2023.100566

Crossref Full Text | Google Scholar

Masarwa, B., Hel-Or, H., and Levy, S. T. (2023). Kindergarten children’s learning of computational thinking with the “Sorting Like a Computer” learning unit. J. Res. Childhood Educ. 38, 165–188. doi: 10.1080/02568543.2023.2221319

Crossref Full Text | Google Scholar

Master, A., Tang, D. J. Z., Forsythe, D., Alexander, T. M., Cheryan, S., and Meltzoff, A. N. (2023). Gender equity and motivational readiness for computational thinking in early childhood. Early Childhood Res. Q. 64, 242–254. doi: 10.1016/j.ecresq.2023.03.004

Crossref Full Text | Google Scholar

Miguel, C. C. (2023). Technology in early childhood education: Digital literacy and unplugged computing. Cadernos CEDES 43, 60–72. doi: 10.1590/cc271211

Crossref Full Text | Google Scholar

Misirli, A., and Komis, V. (2023). Computational thinking in early childhood education: The impact of programming a tangible robot on developing debugging knowledge. Early Childhood Res. Q. 65, 139–158. doi: 10.1016/j.ecresq.2023.05.014

Crossref Full Text | Google Scholar

Mohanarajah, S., and Sritharan, T. (2022). SHOOT2LEARN: Fix-and-play educational game for learning programming; enhancing student engagement by mixing game playing and game programming. J. Informat. Technology Educ. 21, 639–661. doi: 10.28945/5041

Crossref Full Text | Google Scholar

Moltó, M. R., and Martínez, B. A. (2022). Geometric-spatial and computational thinking in early childhood education: A case study with KUBO. Contextos Educativos 41–60. doi: 10.18172/con.5372

Crossref Full Text | Google Scholar

Monteiro, A. F., Miranda-Pinto, M., and Osório, A. J. (2021). Coding as literacy in preschool: A case study. Educ. Sci. 11, 2–15. doi: 10.3390/educsci11050198

Crossref Full Text | Google Scholar

Montuori, C., Pozzan, G., Padova, C., Ronconi, L., Vardanega, T., and Arfé, B. (2023). Combined unplugged and educational robotics training to promote computational thinking and cognitive abilities in preschoolers. Educ. Sci. 13:858. doi: 10.3390/educsci13090858

Crossref Full Text | Google Scholar

Muñoz-Repiso, A. G. V., and Caballero-González, Y. A. (2019). Robotics to develop computational thinking in early childhood education. Comunicar 27, 63–72. doi: 10.3916/C59-2019-06

Crossref Full Text | Google Scholar

Nacher, V., Garcia-Sanjuan, F., and Jaen, J. (2016). Interactive technologies for preschool game-based instruction: Experiences and future challenges. Entertain. Comput. 17, 19–29. doi: 10.1016/j.entcom.2016.07.001

Crossref Full Text | Google Scholar

Nores, M., Friedman-Krauss, A., and Figueras-Daniel, A. (2022). Activity settings, content, and pedagogical strategies in preschool classrooms: Do these influence the interactions we observe? Early Childhood Res. Q. 58, 264–277. doi: 10.1016/j.ecresq.2021.09.011

Crossref Full Text | Google Scholar

Odgaard, A. B. (2022). What is the problem? A situated account of computational thinking as problem-solving in two Danish preschools. KI – Künstliche Intell. 36, 47–57. doi: 10.1007/s13218-021-00752-4

Crossref Full Text | Google Scholar

Odgaard, A. B. (2023). Who programs whom?—Computational empowerment through mastery and appropriation in young children’s computational thinking activities. Int. J. Child-Comput. Interact. 37:100598. doi: 10.1016/j.ijcci.2023.100598

Crossref Full Text | Google Scholar

OECD (2020). Making the most of Technology for Learning and Training in Latin America. Paris: OECD Publishing.

Google Scholar

Otterborn, A., Schönborn, K. J., and Hultén, M. (2020). Investigating preschool educators’ implementation of computer programming in their teaching practice. Early Childhood Educ. J. 48, 253–262. doi: 10.1007/s10643-019-00976-y

Crossref Full Text | Google Scholar

Papadakis, D. S. (2022). Apps to promote computational thinking and coding skills to young age children: A pedagogical challenge for the 21st century learners. Educ. Process. 11, 7–13. doi: 10.22521/edupij.2022.111.1

Crossref Full Text | Google Scholar

Papadakis, S. (2020). Robots and robotics kits for early childhood and first school age. Int. J. Interact. Mob. Technol. 14, 34–56. doi: 10.3991/ijim.v14i18.16631

Crossref Full Text | Google Scholar

Papert, S. (1980). Children, Computers, and Powerful Ideas, Vol. 10. Eugene, OR: Harvester.

Google Scholar

Pérez-Suay, A., García-Bayona, I., Van Vaerenbergh, S., and Pascual-Venteo, A. B. (2023). Assessing a didactic sequence for computational thinking development in early education using educational robots. Educ. Sci. 13:669. doi: 10.3390/educsci13070669

Crossref Full Text | Google Scholar

Pila, S., Aladé, F., Sheehan, K. J., Lauricella, A. R., and Wartella, E. A. (2019). Learning to code via tablet applications: An evaluation of Daisy the Dinosaur and Kodable as learning tools for young children. Comput. Educ. 128, 52–62. doi: 10.1016/j.compedu.2018.09.006

Crossref Full Text | Google Scholar

Pinto, M. S. M., and Osório, A. (2019). Learn to program in preschool: Analysis with the participation scale. Pixel-Bit 133–156. doi: 10.12795/pixelbit.2019.i55.08

Crossref Full Text | Google Scholar

Presser, A. E. L., Young, J. M., Rosenfeld, D., Clements, L. J., Kook, J. F., Sherwood, H., et al. (2023). Data collection and analysis for preschoolers: An engaging context for integrating mathematics and computational thinking with digital tools. Early Childhood Res. Q. 65, 42–56. doi: 10.1016/j.ecresq.2023.05.012

Crossref Full Text | Google Scholar

Pugnali, A., Sullivan, A., and Bers, M. U. (2017). The impact of user interface on young children’s computational thinking. J. Inform. Technol. Educ. 16, 171–193. doi: 10.28945/3768

Crossref Full Text | Google Scholar

Ramírez-Benavides, K., López, G., and Guerrero, L. A. (2016). A mobile application that allows children in the early childhood to program robots. Mob. Inform. Syst. 2016:12. doi: 10.1155/2016/1714350

Crossref Full Text | Google Scholar

Relkin, E., de Ruiter, L., and Bers, M. U. (2020). TechCheck: Development and validation of an unplugged assessment of computational thinking in early childhood education. J. Sci. Educ. Technol. 29, 482–498. doi: 10.1007/s10956-020-09831-x

Crossref Full Text | Google Scholar

Rich, P. J., Bartholomew, S., Daniel, D., Dinsmoor, K., Nielsen, M., Reynolds, C., et al. (2024). Trends in tools used to teach computational thinking through elementary coding. J. Res. Technol. Educ. 56, 269–290. doi: 10.1080/15391523.2022.2121345

Crossref Full Text | Google Scholar

Scherer, R., Siddiq, F., and Viveros, B. S. (2019). The cognitive benefits of learning computer programming: A meta-analysis of transfer effects. J. Educ. Psychol. 111, 764–792. doi: 10.1037/edu0000314

Crossref Full Text | Google Scholar

Silva, E. F., Dembogurski, B. J., and Semaan, G. S. (2023). A literature review of computational thinking in early ages. Int. J. Early Years Educ. 31, 753–772. doi: 10.1080/09669760.2022.2107491

Crossref Full Text | Google Scholar

Silvis, D., Clarke-Midura, J., Shumway, J. F., Lee, V. R., and Mullen, S. (2022). Children caring for robots: Expanding computational thinking frameworks to include a technological ethic of care. Int. J. Child-Comput. Interact. 33:100491. doi: 10.1016/j.ijcci.2022.100491

Crossref Full Text | Google Scholar

Sjödahl, A., and Eckert, A. (2023). Abstracting and decomposing in a visual programming environment. Int. J. Child-Comput. Interact. 36:100573. doi: 10.1016/j.ijcci.2023.100573

Crossref Full Text | Google Scholar

Somuncu, B., and Aslan, D. (2022). Effect of coding activities on preschool children’s mathematical reasoning skills. Educ. Informat. Technol. 27, 877–890. doi: 10.1007/s10639-021-10618-9

PubMed Abstract | Crossref Full Text | Google Scholar

Su, J., and Yang, W. (2023). STEM in early childhood education: A bibliometric analysis. Res. Sci. Technol. Educ. 42, 1020–1041. doi: 10.1080/02635143.2023.2201673

Crossref Full Text | Google Scholar

Su, J., and Zhong, Y. (2022). Artificial intelligence (AI) in early childhood education: Curriculum design and future directions. Comput. Educ. 3:100072. doi: 10.1016/j.caeai.2022.100072

Crossref Full Text | Google Scholar

Sullivan, A., and Umashi Bers, M. (2018). The impact of teacher gender on girls’ performance on programming tasks in early elementary school. J. Inform. Technol. Educ. 17, 153–162. doi: 10.28945/4082

Crossref Full Text | Google Scholar

Sullivan, A., and Bers, M. U. (2012). Gender differences in kindergarteners’ robotics and programming achievement. Int. J. Technol. Design Educ. 23, 691–702. doi: 10.1007/s10798-012-9210-z

Crossref Full Text | Google Scholar

Sullivan, A., and Bers, M. U. (2013). Gender differences in kindergarteners’ robotics and programming achievement. Int. J. Technol. Design Educ. 23, 691–702. doi: 10.1007/s10798-012-9210-z

Crossref Full Text | Google Scholar

Sullivan, A., and Bers, M. U. (2016). Girls, boys, and bots: Gender differences in young children’s performance on robotics and programming tasks. J. Inform. Technol. Educ. 15, 145–165. doi: 10.28945/3547

Crossref Full Text | Google Scholar

Sullivan, A., and Bers, M. U. (2018). The impact of teacher gender on girls’ performance on programming tasks in early elementary school. J. Inform. Technol. Educ. 17, 153–162. doi: 10.28945/4082

Crossref Full Text | Google Scholar

Sun, D., Ouyang, F., Li, Y., and Zhu, C. (2021). Comparing learners’ knowledge, behaviors, and attitudes between two instructional modes of computer programming in secondary education. Int. J. STEM Educ. 8:54. doi: 10.1186/s40594-021-00311-1

PubMed Abstract | Crossref Full Text | Google Scholar

Sung, J. (2022). Assessing young Korean children’s computational thinking: A validation study of two measurements. Educ. Inform. Technol. 27, 12969–12997. doi: 10.1007/s10639-022-11137-x

Crossref Full Text | Google Scholar

Sung, J. H. Y., Lee, J. Y., and Chun, H. Y. (2023). Short-term effects of a classroom-based STEAM program using robotic kits on children in South Korea. Int. J. STEM Educ. 10:26. doi: 10.1186/s40594-023-00417-8

Crossref Full Text | Google Scholar

Tadeu, P., and Brigas, C. (2022). Computational thinking in early childhood education: An analysis through the computer science unplugged. Rev. Int. Formación del Profesorado 98, 153–170. doi: 10.47553/rifop.v98i36.2.94881

Crossref Full Text | Google Scholar

Terroba, M., Ribera, J. M., Lapresa, D., and Anguera, M. T. (2021). Observational analysis of the development of computational thinking in early childhood education (3 years old) through a proposal for solving problems with a ground robot. RED 21.

Google Scholar

Tolksdorf, N. F., Siebert, S., Zorn, I., Horwath, I., and Rohlfing, K. J. (2021). Ethical considerations of applying robots in kindergarten settings: Towards an approach from a macroperspective. Int. J. Soc. Robot. 13, 129–140. doi: 10.1007/s12369-020-00622-3

Crossref Full Text | Google Scholar

Torres, N. B., Luengo González, R., and Torres Carvalho, J. L. (2018). Roamer, a robot in the early childhood classroom for developing basic spatial notions. RISTI Rev. Ibérica Sistemas e Tecnol. Inform. 2018, 14–28. doi: 10.17013/risti.28.14-28

Crossref Full Text | Google Scholar

Unahalekhaka, A., and Bers, M. U. (2022). Evaluating young children’s creative coding: Rubric development and testing for ScratchJr projects. Educ. Inform. Technol. 27, 6577–6597. doi: 10.1007/s10639-021-10873-w

PubMed Abstract | Crossref Full Text | Google Scholar

UNESCO (2024). What you Need to Know about Early Childhood Care and Education. Available online at: https://www.unesco.org/en/early-childhood-education/need-know?hub=70242 (accessed April 26, 2025).

Google Scholar

Urlings, C. C., Coppens, K. M., and Borghans, L. (2019). Measurement of executive functioning using a playful robot in kindergarten. Comput. Sch. 36, 255–273. doi: 10.1080/07380569.2019.1677436

Crossref Full Text | Google Scholar

van den Berghe, R., Oudgenoeg-Paz, O., Verhagen, J., Brouwer, S., de Haas, M., de Wit, J., et al. (2021). Individual differences in children’s (language) learning skills moderate effects of robot-assisted second language learning. Front. Robot. AI 8:676248. doi: 10.3389/frobt.2021.676248

PubMed Abstract | Crossref Full Text | Google Scholar

Vogt, F., and Hollenstein, L. (2021). Exploring digital transformation through pretend play in kindergarten. Br. J. Educ. Technol. 52, 2130–2144. doi: 10.1111/bjet.13142

Crossref Full Text | Google Scholar

Welch, L. E., Shumway, J. F., Clarke-Midura, J., and Lee, V. C. R. (2022). Exploring measurement through coding: Children’s conceptions of a dynamic linear unit with robot coding toys. Educ. Sci. 12:143. doi: 10.3390/educsci12020143

Crossref Full Text | Google Scholar

Wing, J. (2006). Computational thinking. Commun. ACM 49, 33–35. doi: 10.1145/1118178.1118215

Crossref Full Text | Google Scholar

Wing, J. M. (2011). Computational Thinking: What and Why?. Available online at: https://api.semanticscholar.org/CorpusID:63382972 (accessed April 26, 2025).

Google Scholar

Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S. E., and Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Trans. Comput. Educ. 14, 1–16. doi: 10.1145/2576872

Crossref Full Text | Google Scholar

Yang, W. P., Ng, D. T. K., and Gao, H. Y. (2022). Robot programming versus block play in early childhood education: Effects on computational thinking, sequencing ability, and self-regulation. Br. J. Educ. Technol. 53, 1817–1841. doi: 10.1111/bjet.13215

Crossref Full Text | Google Scholar

Zeng, Y., Yang, W., and Bautista, A. (2023). Computational thinking in early childhood education: Reviewing the literature and redeveloping the three-dimensional framework. Educ. Res. Rev. 39:100520. doi: 10.1016/j.edurev.2023.100520

Crossref Full Text | Google Scholar

Zhang, Z., and Crawford, J. (2023). EFL learners’ motivation in a gamified formative assessment: The case of Quizizz. Educ. Inform. Technol. 29, 1–23. doi: 10.1007/s10639-023-12034-7

Crossref Full Text | Google Scholar

Zuo, Y., Che, L., and Zhang, L. (2023). The development of robotics courses for young children under vector space model. PLoS One 18:e0293397 doi: 10.1371/journal.pone.0293397

PubMed Abstract | Crossref Full Text | Google Scholar

Чернобровкин, B. A., Кувшинова, И. A., Тупикина, Д. B., and Бачурин, И. B. (2020). Educational potential of robotics with android-based devices in preschool education. Pers. Nauki i Obrazovaniya 43, 134–149.

Google Scholar

Keywords: computational thinking, early childhood education, educational robotics, cognitive development, teacher training

Citation: Perez Valdes GP, Boude Figueredo O and Vargas Sanchez AD (2025) Integrating computational thinking in children aged 3 to 6: challenges and opportunities in early childhood education. Front. Educ. 10:1535135. doi: 10.3389/feduc.2025.1535135

Received: 26 November 2024; Accepted: 31 July 2025;
Published: 25 August 2025.

Edited by:

Heidi Kloos, University of Cincinnati, United States

Reviewed by:

Anastasia Misirli, University of Patras, Greece
Francesca Granone, University of Stavanger, Norway

Copyright © 2025 Perez Valdes, Boude Figueredo and Vargas Sanchez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Oscar Boude Figueredo, b3NjYXIuYnVvZGVAdW5pc2FiYW5hLmVkdS5jbw==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.