Computational Thinking (CT) is a foundational 21st-century competency, yet its affective architecture remains insufficiently differentiated and undermeasured in educational research. This study developed and validated the Computational Thinking Anxiety Scale (CTAS), a theory-informed instrument designed to assess the multidimensional nature of Computational Thinking Anxiety (CTA) among adolescents. Developed via expert Delphi review and longitudinally validated with 697 Chinese students aged 9–14, the CTAS exhibits a stable eight-factor structure (CFI = 0.974, RMSEA = 0.041). Age-stratified analyses suggested that some CTA facets were represented more consistently across age groups than others, with the 11–12-year group showing greater variability in loading patterns, whereas algorithm-related anxiety remained comparatively stable across cohorts. A 3-month follow-up (N = 387) confirmed high temporal stability (ICC = 0.983; r = .986) with no significant mean-level change. The CTAS demonstrated discriminant validity (trait anxiety r = –.01; gender r = .03) and weak associations with programming experience, but a substantial negative correlation with CT performance (CTP, r = –.43). As a reliable and age-comparable instrument, the CTAS enables affect-aware assessment and intervention design in educational computing environments.

Computational thinking (CT) is an essential 21st -century competency that can be learned in early childhood. While both plugged (digital) and unplugged (non-digital) programming activities are advocated for developing CT in young children, their comparative effectiveness remains inadequately explored. This quasi-experimental study addressed this gap by evaluating the impact of plugged versus unplugged programming on CT skills and programming interest in 104 5-6-year-old kindergarteners from China with no prior exposure. Compared to a business-as-usual control group, both experimental interventions significantly enhanced CT. Crucially, children in the plugged programming environment demonstrated significantly greater gains in CT skills and programming interest than those in the unplugged group. These findings carry important implications for educational practice and policy in the AI era: they provide evidence-based guidance for selecting tools, suggest that plugged approaches offer distinct advantages for CT development, and inform decisions about resource allocation and technology integration in early childhood education. The study further advances our theoretical understanding by identifying feedback mechanisms as a key factor explaining the differential outcomes.

Design thinking (DT) in early childhood education has received increasing attention; however, scalable research is constrained by the lack of DT assessments suitable for young children. To address this gap, we developed and validated a teacher-report scale-the Early Design Thinking Scale (EDTS) in this study. By synthesizing an established DT model with empirical findings from early childhood DT literature, we initially generated a 30-item pool and refined it to a 23-item, five-dimension (empathize, define, ideate, prototype, and test) scale. Research Findings: Using psychometric cross-validation involving 35 teachers and 417 preschoolers (M-age = 4.79, SD = .95), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) supported the retention of the five dimensions and 21 of the 23 items. The refined scale demonstrated good convergent and discriminant validity, with the average variance extracted for each dimension ranging from .72 to .82. McDonald's omega coefficients for the five factors ranged from .92 to .95, indicating good internal reliability. Practice or Policy: The EDTS provides a reliable and valid tool for researchers and practitioners to assess young children's DT. It can support program evaluation and, in turn, inform instructional planning and efforts to promote children's DT learning and development in early childhood settings.

Children as young as three can engage in oral and visual planning when creating tangible objects from materials, yet it remains unclear whether such design competencies can be systematically strengthened in early childhood to cultivate future design thinkers and innovative creators. To evaluate a research-informed design thinking (DT) education program, we conducted a mixed-methods study. The quantitative component involved a quasi-experiment with 133 children (Mage = 5.22, SD = 0.86) from a Chinese kindergarten (intervention group: n = 68; business-as-usual comparison group: n = 65), where the intervention group received an eight-week DT program. Each child’s DT competence was assessed immediately before and after the intervention, followed by play-based interviews with 12 children (six per group). Child-level analyses indicated a statistically significant improvement in overall DT competence favoring the intervention group (Cohen’s d=0.38). However, gains were uneven across specific DT dimensions. Children showed more apparent gains in Empathize, Define, and Prototype, whereas no significant gains were observed in Ideate and Test. Furthermore, the youngest children appeared to show larger gains, whereas gender did not significantly moderate the intervention effects. Qualitative interviews provided insight into the cognitive processes underlying these developmental variations. Together, these preliminary findings may inform DT education research and practice in early childhood settings.

Coding (Computer programming) is becoming essential across fields and argued as a new literacy for all humans. However, there is a lack of concrete understanding of the cognitive interplay between coding and natural language processing. To address this knowledge gap, this scoping review synthesized findings from 30 empirical studies examining the cognitive and neural associations between these two domains. Results indicated that over two-thirds of the studies identified significant cognitive commonalities between coding and natural language processing, while other studies emphasized mechanistic divergences. Evidence for shared mechanisms reveals that coding and natural language processing are intertwined in both cognitive and neural mechanisms. Specifically, coding significantly activates core language-related brain regions, such as inferior frontal gyrus, and evokes event-related potentials similar to those in natural language processing, particularly for grammatical and semantic hierarchical decoding. Also, the neural activation patterns of programmers change significantly with experience, gradually aligning with the neural representation patterns of natural language processing. In contrast, evidence for differential neural mechanisms highlights that coding relies predominantly on the multi-demand system rather than traditional language regions. This review highlights the crucial role of the human language system as the neural foundation for machine language processing and emphasizes the need for further research to explore the shared and distinct mechanisms between the two domains.

The integration of coding into early childhood education has gained traction globally, yet evidence of its efficacy in fostering diverse developmental domains remains limited. This study aimed to (1) assess effects of a coding curriculum on preschoolers' computational thinking (CT), math, language, and social-emotional outcomes, and (2) examine whether effects varied by gender and family socioeconomic status (SES). Participants included 360 preschoolers (M age = 62.9 months; 179 girls, 181 boys). The treatment group comprised 202 children (91 girls), while the control group included 158 children (88 girls). The treatment group received the computing curriculum, involving age-appropriate coding activities, lasting for eight weeks. Outcomes were measured using standardized assessments. Multilevel modeling and ANOVA revealed that the coding curriculum yielded mixed effects: significant gains in spatial skills (d = 0.21) and emotion regulation (d = 0.24), but a null effect on numeracy (d = -0.15). Children from lower-SES backgrounds showed pronounced improvements in CT (d = 0.22) and prosocial skills (d = 0.63) compared to higher-SES peers. No significant differences emerged for language outcomes. Preschool computing education demonstrates potential to enhance spatial reasoning and emotion regulation, with equitable benefits in CT and prosocial skills for disadvantaged children.

Educational robotics can enhance playful learning in early childhood, but integration faces challenges in regions like Hong Kong. This qualitative study explores early childhood teachers' perceptions of a Story-Inspired Robot Programming (SIRP) curriculum through Activity Theory, based on interviews with 22 teachers after eight weeks of implementation. Teachers noted significant improvements in children's engagement and computational thinking, as well as their own professional development. Key enablers included age-appropriate robots and administrative support, while barriers like limited teacher confidence and increased workload were identified. Based on the findings, an ecosystem-driven policy framework is proposed for early childhood teachers' digital empowerment.

To resolve the contested cognitive foundations of programming, we used functional near-infrared spectroscopy (fNIRS) to compare brain activity in 29 coding-naïve adults while they performed three carefully contrasted tasks, each selected to probe a distinct cognitive function: graphical coding (ScratchJr) engaged linguistic parsing and logical-spatial operations; language comprehension targeted semantic and syntactic processing; and mathematical tasks (mental rotation and arithmetic) tapped visuospatial reasoning and numerical cognition. Univariate analyses revealed that coding robustly engaged core language regions, including Wernicke's area and the angular gyrus, with activation patterns statistically indistinguishable from those during natural language comprehension. At the same time, coding also selectively recruited regions linked to mathematical cognition, including the right dorsolateral prefrontal cortex, parietal lobe, and sensorimotor areas. Critically, temporally resolved analyses revealed a dynamic sequential architecture: during the early phase, coding-activated networks overlapped with language comprehension, whereas the later phase was characterized by sustained and enhanced activation in the frontoparietal network associated with executive control and logical reasoning. These findings provide systematic evidence for a temporally dynamic neural foundation of coding: wherein the brain initially leverages language systems for comprehension before progressively engaging mathematical regions for logical reasoning.

The increasing integration of generative artificial intelligence (GenAI) into early learning necessitates critical investigation into its pedagogical implications. This study examined the operationalization of GenAI within child-focused pedagogy and assessed teacher perspectives on its efficacy. Three classrooms, six teachers and their 88 children at a Chinese kindergarten engaged in STEM projects utilizing GenAI. Data triangulation was established through videotaped observations, semi-structured teacher interviews, and artifacts. Research Findings: Evidence revealed that children participated in GenAI-facilitated STEM learning via a pedagogical framework encompassing planning, inquiry iteration with problem scaffolding, creative inquiry, interaction promotion, and sharing/reflection. Teachers held cautiously positive attitudes toward GenAI integration in early STEM, some shifted from initial skepticism to acceptance, and they recognized GenAI as a valuable assistant that supplements their project-related knowledge, provides multimedia materials for children; however, they faced challenges such as children's limited ability to give prompts, GenAI's age-inappropriate outputs, and GenAI's rhetorical questions distracting children, which increased their guidance workload. Practice or Policy: Our findings inform the feasibility of using GenAI to facilitate early STEM learning via a pedagogical framework as practical scaffolding.

The SCALE taxonomy offers a future-oriented framework of educational objectives for the age of artificial intelligence (AI). This conceptual paper asks how these objectives can be enacted through a pedagogy that remains embodied, creative, and connected to human life. We thus integrate SCALE with Arts-Based Pedagogy (ABP), drawing on embodied cognition to conceptualize ABP as a life-connected learning ecology organized through four interrelated dimensions: perception, representation, creative iteration, and resonance. Rather than fixed stages, these dimensions describe how artistic experience enables learners to notice lived phenomena, give ideas perceptible form, revise meaning through making, and reconnect creation with personal social life. Building on this conceptual integration, we propose the AI-empowered ABP for K-12 education and beyond. The pedagogy moves from sensorial perception and artistic representation to feedback and self-reflection, creative making and AI-empowered iteration, and sharing for resonance. We argue that AI may generate text, images, music alternatives, or feedback, but learners retain primary responsibility for intention, interpretation, judgment, and transformation. AI-empowered ABP therefore positions AI as a generative collaborator and facilitator within a human-led, life-connected process of meaning-making, through which SCALE habits of mind can be mobilized within shared inquiry.

This paper explores the integration of AI agents within the Project Approach in early childhood education (ECE) to enhance children's creative learning in the digital age. Addressing the current lack of guidance in this area, the paper conceptualizes how AI agents can be specifically tailored to foster creativity in young learners. Drawing upon theoretical foundations and empirical evidence, it proposes a “Creative Project Approach” that integrates generative AI and robotics in ECE. A pedagogical framework is developed, consisting of five key steps: (1) identifying learning needs, (2) facilitating child-robot interaction with teacher guidance, (3) situating AI and robot use in various learning contexts, (4) determining the appropriate level of automation and creativity, and (5) evaluating learning outcomes. The paper suggests that leveraging generative AI and robotics within the Creative Project Approach holds significant promise for fostering engagement and creativity in AI-native children.

Previous research has highlighted self-efficacy as a core driving belief influencing teachers' pedagogies and, ultimately, children's achievement. However, scarce research has explored how video-mediated learning activities can be organized to improve the self-efficacy beliefs of pre-service preschool teachers. This study investigated the impact of two learning paradigms mediated by online teaching video cases, namely Individual Learning and Heterogeneous Collaborative Learning (partnerships of in-service teachers, pre-service teachers, and university researchers), on the self-efficacy beliefs of pre-service preschool teachers. Fifty pre-service teachers from one Chinese normal college were randomly assigned to two groups. The study adopted a quasi-experimental intervention design with pre-and post-tests. Results indicated that while both groups improved their self-efficacy, participants in the Heterogeneous Collaborative Learning group showed more significant progress. Specifically, participants following the Heterogeneous Collaborative Learning mode made greater gains in the efficacy for instructional strategies and children engagement compared to the Individual Learning mode. Findings imply that providing pre-service teachers with opportunities for collaborative video analysis with in-service teachers and university researchers is a more effective way to help them develop a firm sense of self-efficacy. Teacher educators are encouraged to adopt this effective paradigm to accelerate pre-service preschool teachers' professional transition in future teacher education programs.

This article presents a scoping review of the published studies on coding (or “computer programming”) and computational thinking education for young children in mainland China. A total of 21 eligible publications were included in the final analysis. Results revealed that (a) most studies did not involve a theoretical framework for intervention and data analysis, (b) the effectiveness of coding with a variety of platforms requires further scientific evaluation, and (c) it is in urgent need to train Chinese teachers to achieve intentional teaching for early coding and computational thinking education. Given that China is pursuing its technological and digital transformation of the society, it is essential to provide developmentally and culturally appropriate coding education for young children to enhance their transferable computational thinking and digital literacy.

Computational thinking (CT) is a problem-solving approach rooted in computing principles and has been increasingly promoted in early childhood education (ECE) with robot programming. Yet, few studies have focused on observing how ECE teachers scaffold children to learn CT through interacting with programmable robots. We conducted a video analysis of seven weekly robot programming lessons at a kindergarten in Hong Kong to examine the scaffolding strategies that enhance CT development among 5-year-olds. We adopted a hybrid approach that included open coding through conversational analysis and the use of predefined codes derived from a three-dimensional CT framework to systematically analyse the video data. Results revealed that teachers employed various strategies (i.e., demonstrations, requests, step-by-step questioning, and embodied and unplugged activities) to foster children's understanding of robot programming and CT concepts. These findings highlight the flexibility and effectiveness of dynamic scaffolding strategies in promoting CT development in young children. Lastly, we propose the 3A-CT Teaching Model (integrating CT concepts into three phases for teaching: Aware, Apply and Act) based on the video analysis to inform future research and practice.

Makerspaces are used to promote classroom change and creativity for the 21st century. Building on the learning theory of Constructionism, this intervention study used a curriculum intervention program, "Making a Makerspace" (MM), to integrate the makerspace into Chinese kindergartens. We used a quasi-experimental research design to evaluate the effects of this curriculum intervention, with 120 children enrolled in the experimental classrooms, while the other 111 children enrolled in the waitlist control classrooms. Teacher-report child performance (N = 231) showed that the MM program resulted in significantly higher scores in children's STEM habits of mind in the intervention group, relative to the control group. Analyses of parent-report child behaviors revealed that there was a significant effect of the MM program on post-intervention temperamental surgency. Our evidence shows that such a scalable program encourages and guides teachers to build a positive learning environment for supporting young children's making and thinking in everyday preschool experiences. The makerspace further sets a solid foundation for the development of children's STEM thinking skills and socioemotional skills in a rapidly changing digital society.