Empowering young minds: The future of computational thinking and AI education in early childhood

Abstract

Empowering young minds in today's rapidly evolving technological landscape is crucial for preparing the next generation to confront and embrace the challenges posed by this new era. Central to this mission is the integration of Computational Thinking (CT) within early childhood education, where a focus on understanding technologies, honing problem-solving skills, and fostering positive ways of thinking can shape future innovators. CT, which encompasses essential concepts, practices, and perspectives (Brennan & Resnick, 2012), is emerging as the new literacy in the age of Artificial Intelligence (AI) (Celik, 2023). By embedding CT into early curricula, we can cultivate critical skills in children that enable them to think algorithmically and adapt to technological advancements. Furthermore, creating learning environments that prioritize collaboration and creativity—utilizing technology as a tool for active engagement rather than passive consumption—will help children develop a mindset that not only adapts to change but also drives it. In this way, early childhood education can empower young minds to thrive in a future where technological fluency is essential, ultimately fulfilling the promise of a dynamic, tech-savvy society. Therefore, this special issue presents a couple of cutting-edge studies that examine the current status of early AI and CT education and pave the way for future studies.

The integration of AI into early childhood education is ushering in a transformative era for educational practices and pedagogies (Su & Yang, 2022). In this collection, Berson and Berson (2024) explore this paradigm shift by examining the interplay between AI, historical imagery, and children's creativity through the use of an AI-powered painting tool called CultureCraft. Grounded in constructionist theory and the multimodality of digital play, their research investigates how such technologies can democratize access to cultural heritage resources while promoting children's creative expression, inquiry-based learning, and critical thinking skills. Utilizing a design-based research approach, the study engaged 20 preschool classrooms to assess how the incorporation of CultureCraft could enhance teacher-child interactions and learning experiences. The iterative phases of the research—including initial training, implementation, observation, and feedback—highlight the importance of a collaborative and adaptable framework for introducing educational technology in early childhood contexts.

In tandem with these findings, also in this collection, Yim and Wegerif (2024) address an essential yet often overlooked aspect: teachers' perceptions and acceptance of AI educational tools designed to teach AI literacy to young students. Employing the Technology Acceptance Model, Yim and Wegerif (2024) conducted a mixed-methods study surveying 57 teachers to gauge their views on the usability and value of AI tools, along with the factors influencing their attitudes. The results reveal that teachers generally hold positive perceptions of the benefits of AI tools for enhancing students' knowledge, skills, and responsible behaviors regarding AI. However, barriers remain, such as limited AI knowledge and experience among educators, technical challenges, concerns about the attributes of the tools, inadequate school infrastructure, and worries about the potential negative impacts of prolonged AI-human interaction. The study convincingly argues that understanding teacher acceptance is crucial for successfully implementing AI literacy education.

Complementing these studies, in this collection, Yeter et al. (2024) offer a global perspective through their comprehensive review of the current state of AI literacy education for young learners across various regions, including Asia, Oceania, Europe, and the Americas. They analyze diverse national and regional initiatives aimed at introducing AI concepts and applications, highlighting the pedagogical approaches and technologies employed. The review emphasizes the benefits of fostering AI literacy at an early age, such as enhanced critical thinking, CT skills, and overall cognitive development. Additionally, Yeter et al. (2024) address ethical considerations related to AI education, including concerns about misinformation and the importance of digital citizenship.

Collectively, these studies illuminate the diverse ways in which AI is reshaping early education, revealing both opportunities and challenges that educators, policymakers, and researchers must navigate to effectively harness AI's full potential in nurturing young learners.

A multifaceted model of CT is essential for effectively understanding its potential benefits for children. As illustrated in Figure 1, CT encompasses more than just a single dimension of skills; it represents an organic and systemic framework that includes fundamental concepts, their applications in problem-solving practices, and diverse ways of thinking. Accordingly, early CT education can significantly influence key foundational skills that are vital for lifelong learning and development.

In the evolving landscape of early childhood education, the integration of CT is increasingly recognized as crucial for fostering complex problem-solving and innovative skills. In this collection, Harper et al. (2024) emphasize the importance of incorporating culturally responsive computing (CRC) approaches within early childhood computer science education. Through a design-based research partnership, the authors codeveloped a CRC curriculum for preschoolers called the Culturally Relevant Robotics: A Family and Teacher (CRRAFT) program. Their qualitative content analysis revealed that the CRRAFT program created meaningful opportunities for young Black and Latinx children to develop CT skills in culturally relevant contexts. The findings highlight the focus on coding activities and tools that empower children as innovative technology creators and change agents, contributing to a burgeoning theory of CRC tailored for early childhood education that aims to engage underrepresented groups in computer science from an early age.

Expanding on this concept, in this collection, Hubelbank et al. (2024) introduce a research-practice partnership (RPP) model designed to integrate CT in culturally responsive ways within PreK-5 classrooms. Their study explores the co-constructed processes and frameworks established by educators involved in the RPP, revealing how these frameworks support culturally, linguistically, and developmentally responsive pedagogies. Key findings indicate that participation in the RPP model significantly enhanced early childhood and elementary teachers' knowledge and confidence in implementing CT within their curricula. Teachers reported positive experiences and perceived the professional development they received as valuable for successfully integrating CT and culturally responsive practices.

The significance of parental involvement in nurturing CT skills is underscored by Lim et al. (2024), who explore the “Discovery Play” program, which utilizes open-ended construction play and the engineering design process. This approach not only fosters CT skills—such as decomposition and algorithmic thinking—but also empowers parents, especially those from economically disadvantaged backgrounds, to confidently engage in their children's problem-solving and learning. The study highlights the effectiveness of unplugged, family oriented methods in promoting CT development, particularly for young children from underserved communities who may have limited access to formal learning opportunities.

Meanwhile, in this collection, Lemley and Aladé (2024) investigate parents' perceptions and understanding of CT after engaging with CT-embedded educational media alongside their children. Given the rising emphasis on STEM learning and CT-focused content for young audiences, their study reveals that most participating parents were unfamiliar with the term “computational thinking” and lacked a clear understanding of how their children could learn CT skills—even after exposure to relevant educational media. This finding underscores the need for clearer messaging and scaffolding around CT in children's media, which could bolster parental engagement and reinforcement of CT skills at home.

Additionally, the unique potential of informal learning spaces, such as libraries and museums, offers valuable opportunities for early CT learning through caregiver involvement and collaboration. In this collection, Campana et al. (2024) examine how educators in these informal settings support CT development in young children. By interviewing 18 library and museum educators across the United States, the researchers explored goals for caregiver participation in CT activities and strategies for enabling varied caregiver roles. The findings indicate that educators aim for caregivers to adopt roles ranging from actively supervising and facilitating to collaboratively co-learning with their children. Various approaches, such as providing specific prompts, structured yet open-ended activities, and fostering a collaborative atmosphere, were implemented to reinforce these caregiver roles.

In summary, these studies collectively illustrate the multifaceted nature of CT and the necessity of nurturing it in early childhood through formal, informal, and home-based environments. By embracing culturally responsive, inclusive, and collaborative approaches, educators and caregivers can play a pivotal role in shaping the next generation of computational thinkers.

In this collection, Yang et al. (2024) present a comprehensive review that outlines an ecosystem-driven pathway (Figure 2) for advancing early CT education. Their umbrella review synthesizes findings from 13 prior review studies on integrating CT into early childhood education, providing a thorough analysis of the research landscape. We identify key facets of CT relevant to young learners, including fundamental concepts such as abstraction, algorithms, decomposition, debugging, and control structures. The review also highlights the various tools and platforms used to engage young children in CT learning, ranging from tangible robotics to interactive coding apps. Crucially, we emphasize the necessity of age-appropriate instructional strategies, such as using narratives and physical embodiments, to effectively teach CT skills to this age group. Furthermore, the analysis reveals insights on learning outcomes associated with early CT education, assessment methods, and teacher training programs. By employing an umbrella review methodology, this study not only addresses significant research gaps but also offers actionable guidance for educators and policymakers. It is a vital resource for those aiming to incorporate CT into early childhood curricula, ensuring that young learners are equipped with essential skills for the future.

As the global movement toward AI literacy education gains momentum, this special issue emphasizes the need for comprehensive and accessible AI literacy programs tailored to the diverse needs of young learners. These programs are essential for preparing future generations to thrive in an increasingly AI-driven world (Yeter et al., 2024). While AI presents a promising pedagogical shift in early education, it is critical to prioritize the human element in children's learning. Technology should support, not replace, the vital role of teachers in fostering meaningful and transformative educational experiences (Berson & Berson, 2024).

In the future shaped by AI, only those equipped with CT will possess the ability to tackle complex problems that AI cannot solve. CT is emerging as a key 21st-century literacy that not only enhances knowledge and problem-solving skills but also nurtures important personal traits such as perseverance and the ability to communicate effectively (Yang et al., 2024). This special issue offers valuable insights for researchers and educators dedicated to designing equitable and culturally relevant computing curricula for young learners (e.g., Harper et al., 2024).

To effectively nurture computational thinkers, an ecosystem-driven reform is essential (Figure 2; Yang et al., 2024). Supporting parents as co-facilitators of CT in the home environment is crucial, which can be achieved through home-school collaboration, parent education, and the provision of meaningful media content (Lemley & Aladé, 2024; Lim et al., 2024). Furthermore, understanding teacher perceptions is vital for the development and implementation of programs that enhance student learning in the AI era (Yim & Wegerif, 2024).

Additionally, a research-informed, collaborative approach to teacher professional development emerges as a promising model for bridging the gap between research and practice, particularly in the critical area of CT education (Hubelbank et al., 2024). This approach is especially effective when supported by culturally responsive CT curricula (Harper et al., 2024). By embracing comprehensive, system-wide education reforms, we can establish a solid foundation that equips young learners with the CT skills necessary to succeed in the AI age.

Weipeng Yang: Conceptualization; writing - original draft; writing - review and editing. Jiahong Su: Writing - review and editing. Hui Li: Writing - review and editing.