Education for Sustainability and Artificial Intelligence based on Computational thinking: A Nature Care Play for Early Childhood

Education for Sustainability and Artificial Intelligence based on Computational thinking: A Nature Care Play for Early Childhood | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Education for Sustainability and Artificial Intelligence based on Computational thinking: A Nature Care Play for Early Childhood Jhon Alé, Mariana Rodríguez-Donoso This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5954038/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In line with the principles of peace, justice, and sustainability, Artificial Intelligence (AI) and its subfields, such as Machine Learning, have the potential to make learning more equitable, accessible, and inclusive. To achieve this, it is important to promote educational experiences from early ages that help children understand how AI works, using low-cost, easily accessible resources that are contextualized to real-world problems. This study seeks to contribute to that goal in an underexplored age group by presenting the design of a sequence of learning activities for early childhood education. The sequence combines strategies based on Computational Thinking (CT) and play, focusing on the care of endangered fish species in Chile. Methodologically, a four-stage design-based research approach was followed, including two rounds of evaluation and redesign: first, five experts assessed the content and format of the proposed activities through a closed-question survey and provided open-ended feedback on their choices; then, a second evaluation was carried out in which the activities were implemented with 15 children aged 6 to 8, and the sessions were recorded and photographed. Descriptive, statistical, and content analyses were conducted on the collected data. Overall, the results indicate that the experts positively validated the educational resources in terms of format and content, while also identifying difficulties related to children’s understanding of AI. Across the board, the implementation with children revealed a strong interest in the environmental issue and in its combination with CT-based activities. Artificial Intelligence in Education Early Childhood Education Sustainability Computational Thinking. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Artificial Intelligence (AI), as a discipline and field of study within computer science (Ertel, 2017 ), has significantly improved and diversified its uses and applications in recent years. Current models, which employ deep learning algorithms, offer much more flexible functions that adapt to human needs, transforming the ways people work, interact, and even learn (Broecke, 2023 ; Del Pero, Wyckoff & Vourc'h, 2022; OECD, 2019 ; United Nations, 2024 ). Although there is still no clear consensus on how to approach the teaching of AI in school education—especially in early childhood (Su & Yang, 2023 ; Su et al., 2023 ; Yeter et al., 2024 )—some recent studies have shown that when AI is accompanied by an educational approach such as constructivism, or by active teaching strategies like game-based learning, gamification, or project-based learning, students' learning, motivation, and interest in school subjects improve significantly (Yeter et al., 2024 ; Yang et al., 2025 ; Yeter et al., 2024 ; Deng & Yu, 2023 ; Kit et al., 2022; Kabudi et al., 2021 ; Touretzky et al., 2019 ; Alé & Arancibia, 2025 ; Chiu, 2020 ). Other studies suggest that incorporating teaching activities based on Computational Thinking (CT) into school curricula could be a key and relevant component for future educational programs (Miao & Shiohira, 2024 ; Isoda et al., 2021 ; Araya et al., 2021 ). Moreover, understanding AI through CT appears to be a crucial aspect for the future, as strengthening people’s understanding and trust in these technologies—particularly by grasping the algorithms behind their functioning—could help guide their ethical and trustworthy use (Celik, 2023 ; Yang et al., 2024 ). In the context of early childhood education, major reviews of previous studies have revealed a considerable lack of empirical research and of educational resources, materials, and instruments to guide AI teaching based on CT at these ages. For example, Su and Yang ( 2023 ) conducted a systematic review on the integration of CT in early childhood education and found that a well-designed curriculum and pedagogical approach with carefully developed teaching resources is key to improving the development of early CT knowledge, concepts, and skills; which in turn would support the development of other abilities such as communication, collaboration, and problem-solving. Su and Yang ( 2023 ) also identified that achieving deep learning through CT remains a pending challenge and that more and new educational resources and activities appropriate for its teaching are needed, including assessment instruments as well as criteria to guide the selection of learning tools. Given the absence of instruments to assess CT skills in early childhood, Xiang et al. ( 2025 ) designed an observation instrument to evaluate CT in young children through their interactions with coding or programmable robots. In this case, the quality of interaction with coding games during students’ hands-on work results in higher levels of cognitive development. Kalemkuş & Kalemkuş ( 2025 ) conducted a qualitative study with a phenomenological design to explore students’ perceptions of AI through symbolic (metaphors) and graphic (drawings) expressions. Their analysis showed that primary school children mostly associate AI with humanoid robots (anthropomorphism) and attribute omnipotent qualities to their functionalities, reflecting misconceptions about AI’s ethical and technical limitations. This empirical study highlights the importance of considering and addressing the socio-affective and motivational aspects of young children in the design of educational resources on AI (for example, curiosity versus fear) and of leveraging learning opportunities to conceptually understand its technical workings and ethical scope through age-appropriate analogies. Regarding other contextual aspects that are relevant during the teaching process in early childhood, Yeter et al. ( 2024 ) conducted a narrative review aimed at exploring how AI literacy is being integrated into primary education at the global level. They found a wide range of strategies and educational approaches for teaching AI in early childhood, ranging from technical approaches such as hands-on learning, unplugged activities, gamification, and collaborative learning, to more conceptual and ethical ones that include elements such as Computational Thinking, ethics, social impact, and culturally responsive education for young children. Recent studies have also highlighted that interdisciplinary collaboration, combined with AI tools, could enhance student learning, motivation, and engagement (Yeter et al., 2024 ; Yang et al., 2025 ; Yeter et al., 2024 ; Deng & Yu, 2023 ; Kit et al., 2022; Kabudi et al., 2021 ; Touretzky et al., 2019 ; Alé & Arancibia, 2025 ; Chiu, 2020 ;). While this may already seem evident, collaboration among experts from different disciplines and sectors is essential for tackling complex challenges such as climate change mitigation, resource and energy management, pandemic control, combating diseases like cancer, and other sustainability-driven initiatives (World Economic Forum, 2024; OECD, 2023 ; Amballoor & Naik, 2023 ; Lee et al., 2023 ; Samerkhanova et al., 2023 ; Kamalov et al., 2023 ; Chiu & Chai, 2020 ; Touretzky et al., 2019 ). Complex problems require complex solutions and, as a result, call for the integration of diverse fields of knowledge. In recent years, authorities and governments from different countries have made explicit calls to leverage AI as an ally in climate action (United Nations, 2023). Growing scientific evidence suggests that AI could become an invaluable technological resource for addressing urgent challenges such as climate change, which affects various territories and regions across the planet (World Economic Forum, 2024a ). AI is currently being used for purposes such as: (a) mapping and monitoring changes in ecosystems vulnerable to climate change effects; (b) predicting climate patterns and potential natural disasters, enabling at-risk communities to plan their adaptation and response more effectively; (c) automating processes such as recycling and waste management; and (d) optimizing production, energy efficiency, and water resource management (World Economic Forum, 2024b ). While AI is widely regarded as a powerful tool for accelerating sustainable development agendas, it should not be seen as a panacea capable of solving these issues on its own (World Economic Forum, 2024b ). Maximizing its benefits and minimizing its limitations requires collaborative and multidisciplinary efforts across all sectors of society. For instance, if not used responsibly, AI’s high energy consumption could result in a significant carbon footprint, undermining its potential advantages. Therefore, AI alone will not resolve crises such as climate change or widespread disease outbreaks, but it does provide a valuable tool for accelerating the transition toward a more sustainable future. Addressing the challenges associated with AI and sustainability from early childhood is essential for reducing fears and ensuring a successful long-term implementation. Developing an understanding that allows individuals to engage with AI through CT may be a viable option for ethically and responsibly preparing citizens and future generations who will have to navigate a world where AI and sustainability will become increasingly present in everyday life. In regions such as Africa and South America, where many countries are still developing, responding to these global demands is even more critical, as these regions face greater gaps in the availability of information about AI education in early childhood schools (Miao & Holmes, 2022 ). Chile, in particular, is one of the countries where climate change has generated widely recognized environmental and sociocultural challenges. This is largely due to its vast geography, which includes ecosystems that are highly sensitive to the effects of climate change (Christie & Cárcamo-Ulloa, 2023 ). Chile is highly vulnerable to the climate crisis (Kabir et al., 2023 ; World Bank Group, 2021 ), facing severe consequences such as the spread of forest fires, droughts affecting rivers, wetlands, and lakes, glacier melting, and even floods in desert areas. For Chilean society, learning to develop innovative solutions to address the challenges of climate change with commitment and confidence is an urgent priority. Likewise, educating and raising awareness among children from an early age about issues related to sustainability has become an important and still pending task. Early Childhood Education for Sustainability (ECEfS) aims to foster a harmonious relationship between human beings and nature, shaping resilient individuals who are critically engaged in the face of climate change (Pérez-Borroto et al., 2016 ; Davis, 2009 ; Davis & Elliott, 2024 ). To achieve this, previous studies have emphasized the importance of promoting multidisciplinary work, with concrete actions for climate change mitigation that take into account the diverse local and cultural contexts in which these challenges arise (Dillon & Herman, 2023 ). In other words, integrating AI tools into early childhood education for sustainability (Davis, 2009 ; Davis & Elliott, 2024 ) should promote educational practices that address real problems of sociocultural relevance—problems derived from human activity and adapted to the specific territories and contexts of each region (Tasquier et al., 2022 ; Alam et al., 2023 ). 1.1 CT in Early Childhood There is a type of teaching activity that helps students of all ages understand fundamental ideas and concepts in computer science without the need for computers or internet access. These activities are grounded in CT and have been utilized in computer science education for more than 35 years (Bell et al., 2012 ; ISTE, 2011 ; Nishida et al., 2009 ; Brennan & Resnick, 2012 ). According to Bell et al. ( 2012 ) and Wing ( 2006 ), the activities based on CT foster algorithmic thinking and are particularly suitable for teachers and children. In addition to not requiring computers or internet access, these activities are often combined with games and challenges to mediate interaction and learning in a playful manner. CP activities frequently incorporate simple-physical objects, such as pencil and worksheet, that encourage human interaction, allowing them to discover computing concepts independently. Furthermore, these activities are easy to implement helping to increase access and reduce social inequality associated with the digital divide in early childhood education (Ahmed & Zubayer, 2024 ). From a cognitive and interdisciplinary perspective (Yaşar, 2018 ), the essence of CT lies in how we process information associatively and distributively, in a way that resembles the functioning of the brain. This duality allows for the combination of inductive and deductive reasoning, and connects CT with skills specific to various disciplines such as the natural sciences or engineering. In this sense, CT not only supports the development of abstraction and problem-solving but also strengthens reflection and complex thinking. Within this broader perspective, CT is not only a means of solving problems, but also a way to understand human behavior by drawing on fundamental concepts from computer science. CT encompasses a set of mental tools that reflect the diversity and depth of the field (Wing, 2006 , p. 33). Similarly, ISTE ( 2011 ) argues that CT goes beyond just problem-solving processes, the logical organization and analysis of data, the use of abstractions such as models and simulations, algorithmic thinking, and the implementation of efficient solutions; it also includes developing attitudes such as confidence when facing complexity and the ability to apply this way of thinking to a wide range of situations. In this broader view, CT is not merely a framework for teaching programming or coding, but an organic and systemic approach to approach problem-solving and the diverse ways of thinking that people employ to solve them (Yang et al., 2024 ). These qualities make CT particularly suitable for use with young children, as early childhood is when individuals form the most meaningful connections and experiences that shape lifelong education. In this regard, Harper et al. ( 2024 ) highlights the importance of incorporating CT in early childhood education but emphasizes the need for a culturally responsive approach (Yang et al., 2024 ; Hubelbank et al., 2024 ; Harper et al., 2024 ). Such an approach enables children and school communities to engage actively in solving culturally relevant problems. Similarly, other human-centered approaches also stress the importance of fostering a role for citizens that extends beyond mere technology consumption at the user level. This perspective positions individuals, from an early age, as innovative agents of change and co-creators of technology. In this sense, achieving the ability to "create" such technologies requires an "understanding" of them, which CT can facilitate (Miao & Shiohira, 2024 ). This curricular integration of CT into early childhood education programs and study plans can drive the development of essential 21st-century skills (Yang et al., 2025 ). It also enables children to develop a more conscious, safe, and responsible relationship with technologies such as AI, machine learning, extended realities, advanced robotics, and other systems that will become increasingly prevalent in the future (Yang & Su, 2024; Yang et al., 2024 ; Yang et al., 2025 ; Castro et al., 2024 ). 1.2 CT and AI It is clear that, in order to overcome the predominant technical and instrumental view of AI as merely an external tool, it is necessary to move toward a deep and formative understanding that links it directly with CT. According to Wing ( 2006 ) and Brennan and Resnick ( 2012 ), the essential components of CT—such as abstraction, algorithmic thinking, decomposition, and pattern recognition—enable the construction of various representations that reflect aspects of human cognition. These representations are transferable to the understanding of core AI processes, such as model training (supervised and unsupervised), data representation, iteration, and evidence-based decision making. This connection between CT and AI subfields like model training aligns with certain curricular frameworks for school education (high school, middle school, and primary school). For instance, the framework proposed by APEC InMside (Isoda et al., 2021 ) includes ‘Machine Learning (ML)’ as a key pillar emerging from the intersection of CT and ‘Statistical Thinking’ (Araya et al., 2021 ). This framework also identifies two other foundational pillars: algorithmic thinking and computational modeling (see Fig. 1 ). The pillar of Algorithmic Thinking involves students learning to break down a problem into very simple instructions that any person or computer can follow unambiguously. One of its key ideas is recursive reasoning (Knuth, 1985 ), which arises from identifying repeated patterns in problems. These patterns are described as a set of rules for handling similar situations, avoiding the need to solve the same problem from scratch each time it appears. This type of thinking is frequently used in problem solving and programming, where algorithms can be expressed through pseudocode, logical operators, lists, arrays, and loops (Wing, 2006 ; Knuth, 1997 ). Processes such as debugging, testing, documentation, peer review, and teamwork are key aspects of this pillar. The pillar of Computational Modeling refers to the construction of programmable or computational models, which may include mathematical elements (Tedre & Denning, 2016 ). These models make it possible to represent complex phenomena and simulate their behavior over time. In this pillar, students can begin modeling using concrete physical representations that include boards, cards, tokens, image representations, and simple rules. This process is further enriched by model validation, comparison with real phenomena, debugging, documentation, and collaborative work. The third pillar stems from the main subfield of AI development known as ML (Sheikh et al., 2023 ). This subfield holds a central place in the Fourth Industrial Revolution and represents a paradigm shift in the human-computer relationship: instead of programming code, users train the computer using simpler mechanisms. Most of what is currently understood as AI actually corresponds to ML techniques—an algorithmic field that combines statistics, computer science, and related disciplines. The core idea of ML is that machines can learn from experience (Larson, 2021 ). The main processes involved in ML include identifying relevant data features, defining classes or categories, applying metrics to assess the discriminative power of features (e.g., using scatter plots), building and testing datasets, and measuring the performance of supervised and unsupervised trained classifiers. Numerous examples of AI activities based on CT have been implemented in higher education, middle school, and secondary education (e.g., Lindner et al., 2019 ; Ossovski & Brinkmeier, 2019 ; Geldreich et al., 2016 ; Li et al., 2023 ; Alé-Silva, 2023 ; Araya, 2023 ; Araya et al., 2021 ), some of which are directly linked to one of the three pillars of the APEC InMside framework. However, in early childhood or preschool education, there is still a noticeable lack of well-designed educational experiences for students that incorporate algorithmic thinking, computational modeling, or ML into appropriate and contextualized activities to integrate AI through CT at this level (Su & Yang, 2023 ; Yeter et al., 2024 ; Su & Yang, 2022 ; Labanda-Jaramillo et al., 2022 ). New proposals are needed to allow young children to approach AI comprehension processes from an early age through playful strategies, concrete objects, and age-appropriate methods, supported by new empirical evidence that helps conceptualize the different ways AI can be incorporated to enrich and support early childhood education (Su et al., 2023 ). The study by Kalemkuş and Kalemkuş ( 2025 ) analyzed primary school students' perceptions of AI using metaphors and drawings, revealing varied representations ranging from images of robots and computers to notions of assistance or surveillance. Through this qualitative approach, the authors demonstrate that students already possess mental models of AI, although these are often fragmented and influenced by cultural or media stereotypes. This finding reinforces the need to design early educational experiences that promote more accurate, reflective, and contextualized understandings of AI, in connection with fundamental computational thinking skills. Integrating strategies such as play, modeling, and active exploration can help mediate these initial representations and expand children's conceptual repertoire about how AI works, in line with CT-based approaches that emphasize not only understanding, but also applying and creating with AI (Miao & Shiohira, 2024 ). 2.3 Education for Sustainability in Early Childhood Addressing the urgent need to tackle the socio-environmental crisis, Education for Sustainability (ES) must be integrated into all levels of education, starting with early childhood, the most critical stage for fostering a harmonious relationship between humans and nature (Pérez-Borroto et al., 2016 ; Rodríguez-Silva & Alsina, 2023). Moreover, ES serves as a fundamental pillar for sparking interest in environmental stewardship and promoting ecological behaviors over time, shaping resilient citizens who actively and critically participate in climate change mitigation and adaptation actions (UNESCO, 2020 ). Based on the referenced studies, it is important to note that research gaps exist in Early Childhood Education for Sustainability (ECEfS), particularly regarding direct practice and participatory experiences with children in sustainability contexts (Davis, 2009 ; Davis & Elliott, 2014 ; Güler Yildiz et al., 2021). There is also a need to investigate emerging local situations, enabling immersion in cultures and interaction with diverse forms of knowledge and definitions present within communities (Bascopé et al., 2019 ; Nxumalo & Pacini-Ketchabaw, 2017 ). According to Davis and Elliott ( 2014 ), ECEfS offers opportunities for children to acquire the knowledge, skills, and attitudes needed to identify and address problems relevant to their most significant local contexts. This approach requires a shift toward participatory engagement with children, empowering them to express their opinions and engage responsibly and respectfully with their socio-environmental surroundings, fostering awareness of the daily practices they employ to live (Rodríguez-Donoso et al., 2024 ). Education designed for children must provide every child with the opportunity to explore, question, and debate phenomena and connections in their environments, challenging and nurturing their personal interests (Skolverket, 2018 ). In this context, Borg & Samuelsson ( 2022 ) stress the importance of creating participatory learning environments where children act as agents of change, transforming the socio-environmental settings they inhabit. 2.4 Research Question Considering this background, the core research question guiding the study is: How can a learning sequence be designed for early childhood education that enables children in Chile to explore AI through computational thinking while addressing a sustainability problem contextualized in their social, environmental, and cultural surroundings? To answer this central question, the study sets out the following aim: To design and validate a learning sequence for early childhood education that enables children in Chile to explore AI through CT while solving a sustainability-related problem relevant to their social, environmental, and cultural context. 3. Method This study follows an educational design research approach (Juuti & Lavonen, 2006 ; Plomp & Nievee, 2013). This method focuses on teaching activities (e.g., Alé-Silva & Sánchez, 2024 ; Huerta-Cancino & Alé, 2024) and is based on the implementation of flexible design cycles involving iterative and constant implementation, analysis, and redesign. It does not adhere to a specific educational theory (Easterday et al., 2014 ; Guisasola Aranzabal et al., 2021 ). To achieve the study’s objective, we established four design phases: 3.1 Phase 1: Theoretical Review and Identification of an Environmental Problem First, a theoretical review was conducted to establish foundations for guiding and supporting the design of AI activities based on CT. Various examples of CT activities applied to practical school teaching scenarios were reviewed (e.g., Lindner et al., 2019 ; Ossovski & Brinkmeier, 2019 ; Geldreich et al., 2016 ; Li et al., 2023 ; Alé-Silva, 2023 ; Araya, 2023 ; Araya et al., 2021 ). In addition, educational books on AI were consulted that present practical activities for learning about AI through CT with children (e.g., ReadyAI, 2023 ). This process made it possible to incorporate design elements used in similar educational materials and research within the field. Second, to ensure that the teaching activities were authentic, age-appropriate, and adapted to the region’s social and cultural context, various socio-environmental issues affecting Chile and its people were analyzed. After reviewing global reports and several studies on the topic (e.g., IPCC, 2022 ; Blue Sky, 2023 ; Morales et al., 2022 ; Salcedo-Castro et al., 2023 ; Christie & Cárcamo-Ulloa, 2023 ; Burck et al., 2021 ), environmental problems related to water supply and consumption were selected—mainly because it is one of the most critical issues currently affecting Chile. This process enabled the incorporation of a local territorial issue related to the care and protection of water bodies and the living beings that inhabit them, which are threatened by pollution from waste and other debris generated by human activity. 3.2 Phase 2: Design of Initial Activity Prototypes Next, we conducted multiple discussions and brainstorming sessions to develop initial prototypes of the teaching activities. We created activities resembling class guides for students and tested them internally to assess the dynamics involved. We designed several paper-based prototypes to pictorially represent "categories" for learning about supervised ML within the local context (e.g., classifying types of waste typically generated by children, types of plastics sold in local markets, types of plants or animals from the area, among similar examples). Finally, drawing inspiration from similar models proposed by ReadyAI ( 2023 ) and Lindner et al. ( 2019 ), we decided to create representations for classifying fish as "endangered" and "not endangered." The next step involved refining the variables displayed in each fish representation, ensuring the minimalistic caricatures were based on the real physical features of fish native to Chile. This proved to be a significant challenge as it required careful attention to details such as colors, markings, and fin shapes. Ultimately, we incorporated the most relevant features of some fish species found in local rivers (e.g., Catfish, Puye, Silverside), though we were unable to include certain variables such as size (see Fig. 2 ). 3.3 Phase 3: Pedagogical and Didactic Design In this phase, we proposed various dynamics for engaging students in the activities, integrating the previously designed pictorial representations of fish. We tested different ways of analyzing and grouping fish characteristics for classification as "endangered" or "not endangered." These efforts combined individual and collaborative teaching strategies, utilizing both free and guided play approaches. We developed instructions for educational materials for both teachers and students, including examples and potential answers to the activities to facilitate adaptation to new contexts and scenarios. During this phase, we also defined the approximate durations for each activity and established key evaluation indicators to measure learning objective achievement. 3.4 Phase 4: Evaluation and Redesign Finally, we conducted an evaluation and redesign of the educational activities in two sub-phases. As a general approach, we began by evaluating the activities with adults before directly engaging with children, as an ethical precaution to minimize potential risks. Both processes adhered to ethical principles such as transparency, anonymity, confidentiality, and voluntary withdrawal. Five early childhood education experts evaluated the content and format of the developed materials through a survey (see Appendix A), which led to an initial redesign to improve the educational content and structure. The experts, selected for their extensive classroom experience (at least 15 years), independently assessed the activities using a 4-point Likert scale and provided open-ended feedback to justify their choices. Subsequently, we implemented the activities with 15 children aged 6 to 8 years. The sessions were audio-recorded and supplemented with photographs and field notes. 3.5 Description of the Analysis Process We analyzed the experts' closed-ended responses using a Likert scale through descriptive analysis techniques across the responses of each evaluator. This included calculating means, standard deviations, and identifying maximum and minimum score values, among other metrics. We also compared response trends to verify their consistency using the statistical indicator Kendall’s W (Emerson, 2023 ). This analysis allowed us to assess the level of agreement among evaluators for ordinal scale data such as the Likert scale. Based on these findings, we proceeded to redesign and improve all educational materials. The feedback provided by the experts regarding the activity design, the children's graphical resolution of the experience, the audio recordings, and our field notes were analyzed using inductive content analysis. This type of analysis involves a rigorous and systematic examination of the nature of messages exchanged in communication acts (Krippendorff, 1990 ), based on the content that emerges from the data. To carry out this analysis, we followed the process described by Rodríguez-Donoso & Mauri (2017), which consisted of: (a) an initial reading of the transcriptions, (b) open coding related to the study’s research questions, and (c) construction of analytical categories derived from common codes. The data from the activity guide records, audio recordings, and field notes collected during the work with the children were triangulated to visualize relationships and concordances established throughout the experience. Once the information was analyzed, we proceeded with the redesign of the learning experience. 4. Results 4.1 Results of the Validation by Expert Judgment Regarding the results of the validation surveys conducted by expert judgment, we observed that, overall, the average trend among all experts was either "Agree" or "Strongly Agree" that the educational resources were appropriate in terms of format and content (see Table 1 ). Table 1 Descriptive statistics of the format and content of activities by expert. Expert Format Content Mean SD Mean SD 1 3,60 5,48 3,67 5,16 2 2,60 5,48 2,67 5,16 3 3,40 5,48 3,50 5,48 4 3,40 5,48 3,50 5,48 5 3,60 5,48 3,67 5,16 Regarding the evaluations of each question, we observed that questions related to the format, specifically the clarity in the wording of activity questions (Question 2) and the clarity of graphic resources (Question 3), received the lowest average ratings. In contrast, among the questions related to content, the results for the time allocated for completing the activity (Question 11) received the highest average rating, while the question regarding whether the activities allowed children to understand some basic processes of how ML works, received the lowest average rating (see Table 2 ). Table 2 Descriptive statistics of the format and content of activities by question. Format Content Question (n°) 1 2 3 4 5 6 7 8 9 10 11 Mean 3,4 3,2 3,2 3,4 3,4 3,6 3 3,2 3,6 3,2 3,8 SD 0,55 0,84 0,45 0,89 0,55 0,55 0,71 0,45 0,89 0,45 0,45 Regarding content-related questions, the results for "the time allocated for completing the activities" (Question 11) received the highest average rating, while the question on whether the activities allowed children to understand some basic processes of how ML works (Question 7) received the lowest average rating. Finally, concerning the agreement observed among experts based on Kendall's W indicator, a moderate and significant agreement was identified for both the format (W = 0.536, p < 0.05) and content (W = 0.448, p < 0.05) of the proposed educational activities (see Table 3 ). Table 3 Results from three methods of attribute selection. Format Content N 5 5 Kendall's W 0,536 0,448 Chi-square 10,722 10,722 Degrees of freedom 4 4 Asymptotic significance 0,030 0,029 In relation to the format of the sequence, two categories of agreement among experts were identified: Fish colors: Experts noted that certain colors make it difficult to see the shapes of the fish and distinguish them (experts 1, 2, and 4). The designs of three fish, in particular, were hard to see due to their very light colors. Photographs of the location: Experts suggested using photographs of Cajón del Maipo with polluted areas (experts 1 and 4). Including images relevant to the issue of river waste was deemed pertinent, as this is a primary cause of fish extinction in the area. Regarding the content of the didactic sequence, three analytical categories were identified: The relationship between the game and AI processes: Experts commented that "the analogy with AI gets lost during the experience with the fish" and suggested adding stages of the AI process, providing clear examples of ML, and explaining AI and its characteristics (experts 2, 3, and 4). The final question of the experience: Experts found it to be too abstract, difficult to represent, and hard for children to understand (experts 1, 2, 3, and 4). Explaining certain terms and concepts to children: For example, one expert suggested replacing the term "geographer" with "fish researchers" (experts 1, 2, 4, and 5). Overall, experts emphasized the importance of ensuring comprehension of basic and fundamental concepts to facilitate children's engagement with the activity. Special attention was given to the relationship between environmental experience and the functioning of AI, as this is the central objective of the activity. Additional suggestions, although not repeated by multiple experts, were considered relevant for redesigning the activity. These included avoiding direct reading and proposing questions related to the characteristics of fish, waste, and their relationship. Some experts suggested replacing terms such as "teachers" with "facilitators" and "children" with "childhoods." Regarding didactic aspects, expert 1 recommended diversifying strategies since the fish classification activities primarily focused on visual representation. Additionally, she suggested incorporating mediation to help recall the identified variables for constructing the fish classification models. While most of the experts' suggestions were incorporated, not all were included in the initial redesign. In relation to the format of the sequence, two categories of agreement among experts were identified: Fish colors: Some colors were noted to make it difficult to distinguish the shapes of the fish (experts 1, 2, and 4). The designs of three fish, in particular, were highlighted as hard to discern due to their very light colors. Photographs of the location: Experts suggested including photographs of Cajón del Maipo with polluted areas (experts 1 and 4). The inclusion of images relevant to the issue of river waste was deemed appropriate, as it is the primary cause of fish extinction in the region. Regarding the content of the didactic sequence, three analytical categories emerged: The relationship between the game and AI processes: Experts noted that "the analogy with AI is lost during the fish experience" and recommended including the stages of the AI process, providing clear examples of ML, and explaining AI and its characteristics (experts 2, 3, and 4). The final question in the experience: Experts described it as too abstract, difficult to represent, and challenging for children to grasp (experts 1, 2, 3, and 4). Explaining certain terms and concepts to children: For example, one expert suggested replacing the term "geographer" with "fish researchers" (experts 1, 2, 4, and 5). Overall, the experts emphasized the importance of ensuring the comprehension of basic and fundamental concepts to facilitate children's engagement with the activity. Special attention was given to the relationship between environmental experience and the functioning of AI, as this is the central objective of the activity. Additional suggestions, though not repeated among experts, were considered relevant for redesigning the activity. These included avoiding direct reading and proposing questions related to the characteristics of the fish, waste, and their relationship. Some experts recommended replacing terms like "teachers" with "facilitators" and "children" with "childhoods." Finally, regarding didactic aspects, expert 1 suggested diversifying strategies, as the fish classification activities mainly focused on visual representation. She also recommended mediation to help children recall the identified variables used to construct the fish classification models. While most of the experts' suggestions were incorporated, not all were included in the initial redesign. 4.2 Implementation with Children The learning experience was implemented with 15 children aged 6 to 8 (see Fig. 3 ), and the main results were organized according to the activity stages: During the introductory stage, we observed significant enthusiasm for starting the activity, especially after presenting a description of how machine training works. At this stage, questions such as, "Do we have to read all of this?" were raised, referring to the sheets held by the facilitator. The presentation of the geographical map proved crucial for situating the environmental problem within the children's local context, as did the images of rivers with and without waste. As the experience progressed, the children's initial reaction to observing endangered fish was to group them according to their own observations and classification criteria, often based on fish colors and whiskers. After mediation, the children grouped fish characteristics effortlessly, focusing on features like eyes, tails, dorsal and pectoral fins, and whiskers. Numerical concepts were also introduced during mediation, such as "half of them have whiskers" or "half of them have spots." When asked if they wanted to name any other features, one child noted that the colors of the fish's bellies also matched—a detail none of the experts had identified. The second stage, which involved classifying non-endangered fish, was somewhat quicker. However, younger children (ages 6 and 7) exhibited some anxiety to finish the game quickly. When invited to verify the models they had created in their drawings, their interest and curiosity were reignited. Some children immediately began pointing out which fish were "endangered" without necessarily grouping them. The facilitator suggested they observe the fish calmly and in detail to carry out the classification properly. Most children subsequently evaluated the likelihood of fish being endangered or not with remarkable accuracy and were very pleased with their achievement. In the final stage, during the conclusion of the experience, one of the initial responses to the application question was: "We need to look at the fish that are endangered because if there are endangered fish, it’s because there’s trash." They also noted that it’s important to observe the water's color and whether there’s oil, which is also considered trash. Another suggestion was to check if river animals were trapped by something, as their lack of movement could indicate they were caught in waste. 4.3 Final Design After integrating the results from all evaluations, we have developed the final version of the educational design. The Play is available as an open-access resource on the website ( https://jhonalesilva.github.io/AI-Fish/ ). The aim of this educational design is: "To recognize some of the basic processes of supervised ML through classification, applied to a sustainability problem for children aged 6 to 8 in Chile." The full implementation of the design takes approximately 45–60 minutes and is divided into five secondary activities, all aligned with the supervised machine learning (ML) process and the framework proposed by APEC InMside (Isoda et al., 2021 ): (1) Training with labels, (2) Modeling, (3) Prediction and verification. The first activity focuses on simulating “training with labels.” At this stage, children perform groupings, comparisons, and drawings to classify common characteristics of endangered fish. They create drawings on a template that includes predefined "categories" or "labels" for grouping variables to identify the primary morphological traits of endangered fish (see Fig. 4 ). The second activity involves contextualization. Children take on the role of "fish researchers in the Cajón del Maipo River," identifying some habitat characteristics of endangered fish and reflecting on the problem of river waste (see Fig. 5 ). In the third activity, the "training with labels" simulation is repeated. This time, children group the common characteristics of fish that are not endangered. They then draw each of the shared characteristics (labels) that most of these fish have in common (see Fig. 6 ). The fourth activity is a guessing game. Children observe a new set of images of unknown fish. They attempt to predict whether the fish are endangered using the templates they previously created, replicating the "model testing" stage of supervised ML. At the end of this activity, children compare their predictions and explain their reasoning to their peers (see Fig. 7 ). In the final activity, children connect corresponding "characteristics" to each "unknown fish" with lines to determine, with greater confidence and based on observed trends, whether the fish is endangered (see Fig. 8 ). The activities conclude with an explanation of the main stages of supervised learning, comparing them to the activities the children completed, and reflecting on other applications for environmental conservation. 5. Discussion and Conclusions AI, climate change, and sustainability have become central topics of global interest (United Nations, 2024 ; UNESCO, 2020 ; UNESCO, 2023 ; UN, 2023). However, these subjects remain challenging to address in early childhood education. While the future is inherently unpredictable, keeping student curricula updated with trends such as AI and sustainability seems crucial for adapting, thriving, and shaping what lies ahead. Recent literature reviews highlight this need, increasingly showing that combining AI with active teaching strategies and methodologies enhances students' development of 21st-century skills (e.g., Gerlich, 2025 ; Niño et al., 2024 ; Celik et al., 2024 ). This is also critical for mitigating concerns associated with AI use, such as cognitive passivity or inhibition of critical thinking practices. AI, climate change, and sustainability have become central topics of global interest (United Nations, 2024 ; UNESCO, 2020 ; UNESCO, 2023 ; UN, 2023). However, these topics remain challenging to address in early childhood education. While the future is inherently unpredictable, keeping student curricula updated with trends such as AI and sustainability appears crucial to adapt, thrive, and shape what lies ahead. Recent literature reviews highlight this need, increasingly showing that combining AI with active teaching strategies and methodologies enhances the development of 21st-century skills in students (e.g., Gerlich, 2025 ; Niño et al., 2024 ; Celik et al., 2024 ). This is also essential to mitigate concerns associated with AI use, such as cognitive passivity or the inhibition of critical thinking. At the same time, establishing a direct link between AI and sustainability from an early age would allow for a better understanding of the environmental implications and ethical limitations associated with how AI works—thereby increasing public trust and helping to reduce fears tied to its use. Following recommendations from previous studies and major international frameworks (e.g., Isoda et al., 2021 ; Miao & Shiohira, 2024 ; Miao & Cukurova, 2024 ), we set out to integrate knowledge from Computer Science, Education, and Environmental Sciences (Heeg & Avraamidou, 2023 ; Yang et al., 2025 ; Yeter et al., 2024 ; Deng & Yu, 2023 ; Kit et al., 2022) to design educational resources aimed at students that are tangible, accessible, low-cost, and culturally relevant, in order to address an environmental issue rooted in the national context. This approach to developing tangible resources is also consistent with Yi et al. ( 2024 ), who emphasized the importance of using AI technologies adapted to early childhood education contexts, such as interactive robots, which allow children to explore and understand basic AI principles interactively using concrete materials before progressing to more abstract processes. Similarly, Su & Zhong ( 2022 ) also highlighted the importance of using tangible educational resources and programmable artifacts to foster social interaction and help young children understand AI principles. Among the findings, this study identifies that challenges and difficulties still remain for young children to deeply understand core ML concepts through CT. However, progress in this area is also evident. The results suggest that, despite persistent difficulties, children gradually become familiar with basic notions of statistical and CT related to supervised ML. From their interaction with the designed games, spontaneous and natural notions emerge related to numerical quantities, whole numbers, fractions, and even the concept of probability. While children enjoy learning about the fish that inhabit their local area, they use these mathematical concepts to classify, recognize patterns, and ultimately solve a problem that requires applying prior experience—an approach that aligns with the process of supervised ML. Additionally, it is important to note that these activities were designed, evaluated, and improved by educators, and they addressed a socio-environmental issue tied to the local context and culture. This latter point makes the designed activities more meaningful for learning by connecting with children’s prior knowledge and experiences, which also aligns with Yang’s ( 2022 ) ideas on the need to promote a culturally responsive pedagogy that allows children to meaningfully explore AI technologies. On the other hand, to date, there are few studies that address the design of educational resources for teaching AI and sustainability to young children. One of the few cases we found was, for example, the study by Araya et al. ( 2021 ), which proposed addressing problem-solving related to pandemics, contagion, and data analysis through CT. This lack of empirical studies has resulted in a limited availability of materials for teachers to implement these topics in the classroom and for curriculum designers to develop adequate teaching guidelines. This is consistent with the conclusions of Su & Yang ( 2023 ), who identified that achieving deep learning through CT remains a pending challenge, and that more and newer educational resources and activities appropriate for its teaching are needed—including assessment instruments and criteria to guide the selection of learning tools. The scoping review conducted by Su et al. ( 2023 ) also highlights this absence in curriculum design and teaching guidelines, which poses challenges for educators seeking to integrate AI literacy in early childhood education. This point supports the urgent need to develop resources and pedagogical guidelines that facilitate the integration of AI and sustainability into early education levels. As in the findings by Bell et al. ( 2012 ) and Lindner et al. ( 2019 ), this study demonstrated progress in how to create experiences that help children understand central ideas of AI and ML through CT, with the difference that it emphasized the integration of environmental disciplines and problems related to the water crisis affecting Chile. This focus could help promote the kind of critical digital literacy proposed in the international framework developed by UNESCO, as elaborated by Miao & Shiohira ( 2024 ) and Miao & Cukurova ( 2024 ), where the goal is to prepare both students and teachers not just to use AI, but also to develop a deep and critical understanding of its social and environmental impacts, emphasizing its ethical and responsible use. Parallels were also found with the framework of Labanda-Jaramillo et al. ( 2022 ) and Sanusi et al. ( 2021 ), which promotes the teaching and learning of AI in K-12 education through CT-based activities. Finally, based on the evaluations and results obtained, it is concluded that the process of designing and validating a learning sequence to teach AI and sustainability in early childhood education in Chile was a successful first step. This sequence allows children to learn and become familiar with cross-cutting concepts of supervised ML through CT, statistical thinking, play, and problem-solving related to sustainability, all adapted to their social, environmental, and cultural context. 6. Limitations and Future Work This study presents a sampling limitation regarding the number of children who participated in the implementation of the designed activity sequence. Therefore, it is important to consider that the evidence included in this study may have been influenced by the small sample size. In light of this, it becomes relevant for future research to also explore the integration of AI and sustainability, but with larger student samples and with evaluation strategies and instruments for CT validated for early childhood, such as those proposed by Xiang et al. ( 2025 ), which enable new assessments based on classroom interaction observations. At the same time, it is important to note that this study did not consider the participation of other key agents in the teaching process of young children. Therefore, from an ecological perspective, future research should also include the involvement of teachers and families in both the design and evaluation processes of teaching resources, as teachers and families are the individuals most actively involved in children's lives. On the other hand, although some limitations still exist regarding students’ difficulties in conceptualizing the stages of AI, we hope that the strategies and combinations used in our educational design may serve as a model to guide future empirical research. Such research could deepen and propose more and new teaching activities aimed at addressing this challenge. This point should also be reflected in future studies that explore new social and environmental issues relevant to teaching and school curricula, while also addressing other ethical and legal implications related to the regulation and scope of these technologies, in order to foster trust from an early age. Declarations Clinical Trial Number Not applicable. Data Availability The datasets generated during and/or analyzed during the current study are not publicly available due to the need to protect and preserve respondents’ confidentiality. However, they are available from the corresponding author upon reasonable request. Ethics declarations The project was reviewed and approved by the University of Chile. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Consent to Participate Informed consent was obtained from all individual participants included in the study. Written informed consent was obtained from the parents or legal guardians of the child participants. Additionally, assent was obtained from the child participants prior to their involvement in the study. Consent for Publication The authors affirm that human research participants provided informed consent and assent for publication of the data collected during the study. Parents or legal guardians of the child participants signed informed consent regarding publishing their children's data. Contributions Conceptualization, J.A.; methodology, J.A. and M.R.-D. formal analysis, J.A.; investigation, J.A. and M.R.-D.; data curation, J.A.; writing—original draft preparation, J.A.; writing—review and editing, J.A.; supervision, J.A.; project administration, J.A.; funding acquisition, J.A. and M.R.-D. All authors have read and agreed to the published version of the manuscript. Competing interests The authors declare no competing interests. Funding This work has been developed with the support of ANID BECAS/DOCTORADO NACIONAL 21240783 and ANID BECAS/DOCTORADO NACIONAL 21241617. References Alé, J., & Arancibia, M. L. (2025). Emerging Technology-Based Motivational Strategies: A Systematic Review with Meta-Analysis. Education Sciences, 15 (2), 197. https://doi.org/10.3390/educsci15020197 Alé-Silva, J., & Sánchez, J. (2024). Environmental robotics for educational revival in hospital classrooms. In K. Miesenberger, P. Peňáz, & M. 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Computers And Education Artificial Intelligence, 3 , 100072. https://doi.org/10.1016/j.caeai.2022.100072 Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education. Artificial Intelligence, 3, 100049. https://doi.org/10.1016/j.caeai.2022.100049 Su, J., & Yang, W. (2023). A systematic review of integrating computational thinking in early childhood education. Computers And Education Open, 4, 100122. https://doi.org/10.1016/j.caeo.2023.100122 Tasquier, G., Knain, E., & Jornet, A. (2022). Scientific literacies for change making: Equipping the young to tackle current societal challenges. Frontiers in Education, 7(689329). https://doi.org/10.3389/feduc.2022.689329 Tedre, M., & Denning, P. J. (2016). The long quest for computational thinking. In Proceedings of the 16th Koli Calling International Conference on Computing Education Research (pp. 120–129). Association for Computing Machinery. https://doi.org/10.1145/2999541.2999542​ Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? In Blue sky talk at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). https://doi.org/10.1609/aaai.v33i01.33019795 UNESCO. (2020). Educación para el desarrollo sostenible. Hoja de ruta. UNESCO. (2023). Education in the age of artificial intelligence. United Nations (UN). (2023). Challenge launched at COP28 to harness artificial intelligence for climate action in developing countries. Williams, R., Ali, S., Devasia, N., DiPaola, D., Hong, J., Kaputsos, S., Jordan, B., & Breazeal, C. (2022). AI + ethics curricula for middle school youth: Lessons learned from three project-based curricula. International Journal of Artificial Intelligence in Education, 33(2), 325–383. https://doi.org/10.1007/s40593-022-00298-y Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215 World Bank Group. (2021). Climate risk country profile: Chile. World Economic Forum. (2024a). 9 ways AI is helping tackle climate change. World Economic Forum (2024b). Emerging Technologies: AI is an accelerator for sustainability — but it is not a silver bullet. https://www.weforum.org/stories/2024/09/ai-accelerator-sustainability-silver-bullet-sdim/ World Meteorological Organization (WMO). (2023). Climate change indicators reached record levels in 2023. Xiang, S., Li, J. W., & Yang, W. (2025). Developing a robot-based computational thinking assessment for young children. Education And Information Technologies. https://doi.org/10.1007/s10639-025-13377-z Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers And Education Artificial Intelligence, 3 , 100061. https://doi.org/10.1016/j.caeai.2022.100061 Yang, W., Su, J., & Li, H. (2024). Empowering young minds: The future of computational thinking and AI education in early childhood. Future In Educational Research. https://doi.org/10.1002/fer3.69 Yang, W., Liang, L., Xiang, S., & Yeter, I. H. (2025). Making a Makerspace in Early Childhood Education: Effects on Children’s STEM Thinking Skills and Emotional Development. Thinking Skills And Creativity, 101754. https://doi.org/10.1016/j.tsc.2025.101754 Yaşar, O. (2018). A new perspective on computational thinking. Communications Of The ACM, 61(7), 33–39. https://doi.org/10.1145/3214354 Yeter, I. H., Yang, W., & Sturgess, J. B. (2024). Global initiatives and challenges in integrating artificial intelligence literacy in elementary education: Mapping policies and empirical literature. Future In Educational Research. https://doi.org/10.1002/fer3.59 Yi, H., Liu, T., & Lan, G. (2024). The key artificial intelligence technologies in early childhood education: a review. Artificial Intelligence Review, 57 (1). https://doi.org/10.1007/s10462-023-10637-7 Zheng, Q. (2022). Integrating computational thinking into a longitudinal data analysis course for public health students. Discover Education, 1 (1). https://doi.org/10.1007/s44217-022-00015-w Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5954038","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448704952,"identity":"eb16259e-a59f-46d9-8a5a-b267db03b990","order_by":0,"name":"Jhon Alé","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACxhlgyoKBH8I/wMMHoh4Q1iLBINkA1cIGohLwWSMBJQ0OQLQwENTCPLv52AOGGgk54xvpF5h5au7IsLE3MH7Ap4VxzrF0A4ZjEsZmN3IKmHmOPeNh4znALIFXy4wcMwkGNonEbTdyEph5Gw7zsEkksOF1GETLP4n6zTNgWuQfEKGFsU0iwUAi/QDUFgZCWtLSJBL7JAxnnHnDcHDOMaAWnsRmvH4xnJF8TOLDNxt5/vb0hw/e1By252c/fPDDB3xaGhhgscADjRoGxgY8GhgY5BFM9gd4VY6CUTAKRsHIBQAPTEZEPzz6IgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chile","correspondingAuthor":true,"prefix":"","firstName":"Jhon","middleName":"","lastName":"Alé","suffix":""},{"id":448704953,"identity":"f21c5de8-28f9-4394-a642-b381c2de8bb5","order_by":1,"name":"Mariana Rodríguez-Donoso","email":"","orcid":"","institution":"University of Chile","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Rodríguez-Donoso","suffix":""}],"badges":[],"createdAt":"2025-02-03 23:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5954038/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5954038/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82051903,"identity":"eadd2550-8212-4c22-8d37-937c69f58c1c","added_by":"auto","created_at":"2025-05-06 10:06:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97679,"visible":true,"origin":"","legend":"\u003cp\u003eAPEC Curriculum Framework Model for Era of AI and Big Data.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/3b3c696528ebd9c0a99968c3.png"},{"id":82051904,"identity":"79d03d22-60a4-4828-ba89-ab367cade148","added_by":"auto","created_at":"2025-05-06 10:06:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177773,"visible":true,"origin":"","legend":"\u003cp\u003eProcess of creating the native fish cards of Chile.\u003c/p\u003e","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/698e2a0d884d0fd90fbe60fe.jpeg"},{"id":82050663,"identity":"40969b8c-e439-474a-b53e-61344e0904e4","added_by":"auto","created_at":"2025-05-06 09:58:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":804935,"visible":true,"origin":"","legend":"\u003cp\u003eImages of children engaging in fish classification activities.\u003c/p\u003e","description":"","filename":"Fig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/b1cffa552b1c24e6c7ed0f26.jpeg"},{"id":82050666,"identity":"e83d0b2c-e185-4566-a9e6-7accbf436090","added_by":"auto","created_at":"2025-05-06 09:58:00","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":304431,"visible":true,"origin":"","legend":"\u003cp\u003eCards of endangered fish from Cajón del Maipo and drawing template.\u003c/p\u003e","description":"","filename":"Fig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/ec7d048b4837e722653a9f45.jpeg"},{"id":82054528,"identity":"0e5a7464-9434-41df-b7e1-f60cee1ac45e","added_by":"auto","created_at":"2025-05-06 10:22:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":640735,"visible":true,"origin":"","legend":"\u003cp\u003eContext and problem of the study situation.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/aac21b6b7ac10448f3f498d0.jpg"},{"id":82051906,"identity":"fbebc763-e9db-4b73-a07d-5b89b16bbcd4","added_by":"auto","created_at":"2025-05-06 10:06:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":278145,"visible":true,"origin":"","legend":"\u003cp\u003eCards of non-endangered fish from Cajón del Maipo and drawing template.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/b64a74566a4a64f8087e6de2.jpg"},{"id":82055958,"identity":"12d69380-cf0c-49d3-a5eb-fa98470ed007","added_by":"auto","created_at":"2025-05-06 10:30:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":332728,"visible":true,"origin":"","legend":"\u003cp\u003eCards of new fish from Cajón del Maipo.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/d67855cb37061ed38e73944f.jpg"},{"id":82050671,"identity":"85189cf4-95f0-4324-9093-0df5ead6c11a","added_by":"auto","created_at":"2025-05-06 09:58:00","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":199440,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of the closing activity: connecting with lines.\u003c/p\u003e","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/a0e11f868d099374f2fe77e8.jpg"},{"id":82055963,"identity":"4b2a1883-51f8-4d47-bbae-c21968ef78c3","added_by":"auto","created_at":"2025-05-06 10:30:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3729876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/e1fece1e-ebc1-4076-be70-f1633843b477.pdf"},{"id":82050661,"identity":"8314113a-4484-4c47-8d9c-fc9bba77d0c0","added_by":"auto","created_at":"2025-05-06 09:58:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19850,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5954038/v1/c583de1fbf135312a8248948.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Education for Sustainability and Artificial Intelligence based on Computational thinking: A Nature Care Play for Early Childhood","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial Intelligence (AI), as a discipline and field of study within computer science (Ertel, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), has significantly improved and diversified its uses and applications in recent years. Current models, which employ deep learning algorithms, offer much more flexible functions that adapt to human needs, transforming the ways people work, interact, and even learn (Broecke, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Del Pero, Wyckoff \u0026amp; Vourc'h, 2022; OECD, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; United Nations, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although there is still no clear consensus on how to approach the teaching of AI in school education\u0026mdash;especially in early childhood (Su \u0026amp; Yang, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026mdash;some recent studies have shown that when AI is accompanied by an educational approach such as constructivism, or by active teaching strategies like game-based learning, gamification, or project-based learning, students' learning, motivation, and interest in school subjects improve significantly (Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Deng \u0026amp; Yu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kit et al., 2022; Kabudi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Touretzky et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Al\u0026eacute; \u0026amp; Arancibia, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chiu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther studies suggest that incorporating teaching activities based on Computational Thinking (CT) into school curricula could be a key and relevant component for future educational programs (Miao \u0026amp; Shiohira, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Isoda et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Araya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, understanding AI through CT appears to be a crucial aspect for the future, as strengthening people\u0026rsquo;s understanding and trust in these technologies\u0026mdash;particularly by grasping the algorithms behind their functioning\u0026mdash;could help guide their ethical and trustworthy use (Celik, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of early childhood education, major reviews of previous studies have revealed a considerable lack of empirical research and of educational resources, materials, and instruments to guide AI teaching based on CT at these ages. For example, Su and Yang (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a systematic review on the integration of CT in early childhood education and found that a well-designed curriculum and pedagogical approach with carefully developed teaching resources is key to improving the development of early CT knowledge, concepts, and skills; which in turn would support the development of other abilities such as communication, collaboration, and problem-solving. Su and Yang (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also identified that achieving deep learning through CT remains a pending challenge and that more and new educational resources and activities appropriate for its teaching are needed, including assessment instruments as well as criteria to guide the selection of learning tools. Given the absence of instruments to assess CT skills in early childhood, Xiang et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) designed an observation instrument to evaluate CT in young children through their interactions with coding or programmable robots. In this case, the quality of interaction with coding games during students\u0026rsquo; hands-on work results in higher levels of cognitive development. Kalemkuş \u0026amp; Kalemkuş (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) conducted a qualitative study with a phenomenological design to explore students\u0026rsquo; perceptions of AI through symbolic (metaphors) and graphic (drawings) expressions. Their analysis showed that primary school children mostly associate AI with humanoid robots (anthropomorphism) and attribute omnipotent qualities to their functionalities, reflecting misconceptions about AI\u0026rsquo;s ethical and technical limitations. This empirical study highlights the importance of considering and addressing the socio-affective and motivational aspects of young children in the design of educational resources on AI (for example, curiosity versus fear) and of leveraging learning opportunities to conceptually understand its technical workings and ethical scope through age-appropriate analogies.\u003c/p\u003e \u003cp\u003eRegarding other contextual aspects that are relevant during the teaching process in early childhood, Yeter et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a narrative review aimed at exploring how AI literacy is being integrated into primary education at the global level. They found a wide range of strategies and educational approaches for teaching AI in early childhood, ranging from technical approaches such as hands-on learning, unplugged activities, gamification, and collaborative learning, to more conceptual and ethical ones that include elements such as Computational Thinking, ethics, social impact, and culturally responsive education for young children.\u003c/p\u003e \u003cp\u003eRecent studies have also highlighted that interdisciplinary collaboration, combined with AI tools, could enhance student learning, motivation, and engagement (Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Deng \u0026amp; Yu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kit et al., 2022; Kabudi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Touretzky et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Al\u0026eacute; \u0026amp; Arancibia, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chiu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;). While this may already seem evident, collaboration among experts from different disciplines and sectors is essential for tackling complex challenges such as climate change mitigation, resource and energy management, pandemic control, combating diseases like cancer, and other sustainability-driven initiatives (World Economic Forum, 2024; OECD, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Amballoor \u0026amp; Naik, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Samerkhanova et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kamalov et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chiu \u0026amp; Chai, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Touretzky et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Complex problems require complex solutions and, as a result, call for the integration of diverse fields of knowledge.\u003c/p\u003e \u003cp\u003eIn recent years, authorities and governments from different countries have made explicit calls to leverage AI as an ally in climate action (United Nations, 2023). Growing scientific evidence suggests that AI could become an invaluable technological resource for addressing urgent challenges such as climate change, which affects various territories and regions across the planet (World Economic Forum, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). AI is currently being used for purposes such as: (a) mapping and monitoring changes in ecosystems vulnerable to climate change effects; (b) predicting climate patterns and potential natural disasters, enabling at-risk communities to plan their adaptation and response more effectively; (c) automating processes such as recycling and waste management; and (d) optimizing production, energy efficiency, and water resource management (World Economic Forum, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). While AI is widely regarded as a powerful tool for accelerating sustainable development agendas, it should not be seen as a panacea capable of solving these issues on its own (World Economic Forum, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Maximizing its benefits and minimizing its limitations requires collaborative and multidisciplinary efforts across all sectors of society. For instance, if not used responsibly, AI\u0026rsquo;s high energy consumption could result in a significant carbon footprint, undermining its potential advantages. Therefore, AI alone will not resolve crises such as climate change or widespread disease outbreaks, but it does provide a valuable tool for accelerating the transition toward a more sustainable future.\u003c/p\u003e \u003cp\u003eAddressing the challenges associated with AI and sustainability from early childhood is essential for reducing fears and ensuring a successful long-term implementation. Developing an understanding that allows individuals to engage with AI through CT may be a viable option for ethically and responsibly preparing citizens and future generations who will have to navigate a world where AI and sustainability will become increasingly present in everyday life. In regions such as Africa and South America, where many countries are still developing, responding to these global demands is even more critical, as these regions face greater gaps in the availability of information about AI education in early childhood schools (Miao \u0026amp; Holmes, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChile, in particular, is one of the countries where climate change has generated widely recognized environmental and sociocultural challenges. This is largely due to its vast geography, which includes ecosystems that are highly sensitive to the effects of climate change (Christie \u0026amp; C\u0026aacute;rcamo-Ulloa, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Chile is highly vulnerable to the climate crisis (Kabir et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; World Bank Group, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), facing severe consequences such as the spread of forest fires, droughts affecting rivers, wetlands, and lakes, glacier melting, and even floods in desert areas.\u003c/p\u003e \u003cp\u003eFor Chilean society, learning to develop innovative solutions to address the challenges of climate change with commitment and confidence is an urgent priority. Likewise, educating and raising awareness among children from an early age about issues related to sustainability has become an important and still pending task. Early Childhood Education for Sustainability (ECEfS) aims to foster a harmonious relationship between human beings and nature, shaping resilient individuals who are critically engaged in the face of climate change (P\u0026eacute;rez-Borroto et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Davis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Davis \u0026amp; Elliott, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To achieve this, previous studies have emphasized the importance of promoting multidisciplinary work, with concrete actions for climate change mitigation that take into account the diverse local and cultural contexts in which these challenges arise (Dillon \u0026amp; Herman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In other words, integrating AI tools into early childhood education for sustainability (Davis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Davis \u0026amp; Elliott, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) should promote educational practices that address real problems of sociocultural relevance\u0026mdash;problems derived from human activity and adapted to the specific territories and contexts of each region (Tasquier et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 CT in Early Childhood\u003c/h2\u003e \u003cp\u003eThere is a type of teaching activity that helps students of all ages understand fundamental ideas and concepts in computer science without the need for computers or internet access. These activities are grounded in CT and have been utilized in computer science education for more than 35 years (Bell et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; ISTE, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nishida et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Brennan \u0026amp; Resnick, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). According to Bell et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Wing (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the activities based on CT foster algorithmic thinking and are particularly suitable for teachers and children. In addition to not requiring computers or internet access, these activities are often combined with games and challenges to mediate interaction and learning in a playful manner. CP activities frequently incorporate simple-physical objects, such as pencil and worksheet, that encourage human interaction, allowing them to discover computing concepts independently. Furthermore, these activities are easy to implement helping to increase access and reduce social inequality associated with the digital divide in early childhood education (Ahmed \u0026amp; Zubayer, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a cognitive and interdisciplinary perspective (Yaşar, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the essence of CT lies in how we process information associatively and distributively, in a way that resembles the functioning of the brain. This duality allows for the combination of inductive and deductive reasoning, and connects CT with skills specific to various disciplines such as the natural sciences or engineering. In this sense, CT not only supports the development of abstraction and problem-solving but also strengthens reflection and complex thinking. Within this broader perspective, CT is not only a means of solving problems, but also a way to understand human behavior by drawing on fundamental concepts from computer science. CT encompasses a set of mental tools that reflect the diversity and depth of the field (Wing, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, p. 33). Similarly, ISTE (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) argues that CT goes beyond just problem-solving processes, the logical organization and analysis of data, the use of abstractions such as models and simulations, algorithmic thinking, and the implementation of efficient solutions; it also includes developing attitudes such as confidence when facing complexity and the ability to apply this way of thinking to a wide range of situations. In this broader view, CT is not merely a framework for teaching programming or coding, but an organic and systemic approach to approach problem-solving and the diverse ways of thinking that people employ to solve them (Yang et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese qualities make CT particularly suitable for use with young children, as early childhood is when individuals form the most meaningful connections and experiences that shape lifelong education. In this regard, Harper et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlights the importance of incorporating CT in early childhood education but emphasizes the need for a culturally responsive approach (Yang et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hubelbank et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Harper et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such an approach enables children and school communities to engage actively in solving culturally relevant problems.\u003c/p\u003e \u003cp\u003eSimilarly, other human-centered approaches also stress the importance of fostering a role for citizens that extends beyond mere technology consumption at the user level. This perspective positions individuals, from an early age, as innovative agents of change and co-creators of technology. In this sense, achieving the ability to \"create\" such technologies requires an \"understanding\" of them, which CT can facilitate (Miao \u0026amp; Shiohira, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis curricular integration of CT into early childhood education programs and study plans can drive the development of essential 21st-century skills (Yang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It also enables children to develop a more conscious, safe, and responsible relationship with technologies such as AI, machine learning, extended realities, advanced robotics, and other systems that will become increasingly prevalent in the future (Yang \u0026amp; Su, 2024; Yang et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Castro et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 CT and AI\u003c/h2\u003e \u003cp\u003eIt is clear that, in order to overcome the predominant technical and instrumental view of AI as merely an external tool, it is necessary to move toward a deep and formative understanding that links it directly with CT. According to Wing (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Brennan and Resnick (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the essential components of CT\u0026mdash;such as abstraction, algorithmic thinking, decomposition, and pattern recognition\u0026mdash;enable the construction of various representations that reflect aspects of human cognition. These representations are transferable to the understanding of core AI processes, such as model training (supervised and unsupervised), data representation, iteration, and evidence-based decision making. This connection between CT and AI subfields like model training aligns with certain curricular frameworks for school education (high school, middle school, and primary school). For instance, the framework proposed by APEC InMside (Isoda et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) includes \u0026lsquo;Machine Learning (ML)\u0026rsquo; as a key pillar emerging from the intersection of CT and \u0026lsquo;Statistical Thinking\u0026rsquo; (Araya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This framework also identifies two other foundational pillars: algorithmic thinking and computational modeling (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe pillar of Algorithmic Thinking involves students learning to break down a problem into very simple instructions that any person or computer can follow unambiguously. One of its key ideas is recursive reasoning (Knuth, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), which arises from identifying repeated patterns in problems. These patterns are described as a set of rules for handling similar situations, avoiding the need to solve the same problem from scratch each time it appears. This type of thinking is frequently used in problem solving and programming, where algorithms can be expressed through pseudocode, logical operators, lists, arrays, and loops (Wing, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Knuth, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Processes such as debugging, testing, documentation, peer review, and teamwork are key aspects of this pillar.\u003c/p\u003e \u003cp\u003eThe pillar of Computational Modeling refers to the construction of programmable or computational models, which may include mathematical elements (Tedre \u0026amp; Denning, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These models make it possible to represent complex phenomena and simulate their behavior over time. In this pillar, students can begin modeling using concrete physical representations that include boards, cards, tokens, image representations, and simple rules. This process is further enriched by model validation, comparison with real phenomena, debugging, documentation, and collaborative work.\u003c/p\u003e \u003cp\u003eThe third pillar stems from the main subfield of AI development known as ML (Sheikh et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This subfield holds a central place in the Fourth Industrial Revolution and represents a paradigm shift in the human-computer relationship: instead of programming code, users train the computer using simpler mechanisms. Most of what is currently understood as AI actually corresponds to ML techniques\u0026mdash;an algorithmic field that combines statistics, computer science, and related disciplines. The core idea of ML is that machines can learn from experience (Larson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The main processes involved in ML include identifying relevant data features, defining classes or categories, applying metrics to assess the discriminative power of features (e.g., using scatter plots), building and testing datasets, and measuring the performance of supervised and unsupervised trained classifiers.\u003c/p\u003e \u003cp\u003eNumerous examples of AI activities based on CT have been implemented in higher education, middle school, and secondary education (e.g., Lindner et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ossovski \u0026amp; Brinkmeier, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Geldreich et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Al\u0026eacute;-Silva, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Araya, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Araya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), some of which are directly linked to one of the three pillars of the APEC InMside framework. However, in early childhood or preschool education, there is still a noticeable lack of well-designed educational experiences for students that incorporate algorithmic thinking, computational modeling, or ML into appropriate and contextualized activities to integrate AI through CT at this level (Su \u0026amp; Yang, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Su \u0026amp; Yang, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Labanda-Jaramillo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). New proposals are needed to allow young children to approach AI comprehension processes from an early age through playful strategies, concrete objects, and age-appropriate methods, supported by new empirical evidence that helps conceptualize the different ways AI can be incorporated to enrich and support early childhood education (Su et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study by Kalemkuş and Kalemkuş (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) analyzed primary school students' perceptions of AI using metaphors and drawings, revealing varied representations ranging from images of robots and computers to notions of assistance or surveillance. Through this qualitative approach, the authors demonstrate that students already possess mental models of AI, although these are often fragmented and influenced by cultural or media stereotypes. This finding reinforces the need to design early educational experiences that promote more accurate, reflective, and contextualized understandings of AI, in connection with fundamental computational thinking skills. Integrating strategies such as play, modeling, and active exploration can help mediate these initial representations and expand children's conceptual repertoire about how AI works, in line with CT-based approaches that emphasize not only understanding, but also applying and creating with AI (Miao \u0026amp; Shiohira, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Education for Sustainability in Early Childhood\u003c/h2\u003e \u003cp\u003eAddressing the urgent need to tackle the socio-environmental crisis, Education for Sustainability (ES) must be integrated into all levels of education, starting with early childhood, the most critical stage for fostering a harmonious relationship between humans and nature (P\u0026eacute;rez-Borroto et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rodr\u0026iacute;guez-Silva \u0026amp; Alsina, 2023). Moreover, ES serves as a fundamental pillar for sparking interest in environmental stewardship and promoting ecological behaviors over time, shaping resilient citizens who actively and critically participate in climate change mitigation and adaptation actions (UNESCO, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the referenced studies, it is important to note that research gaps exist in Early Childhood Education for Sustainability (ECEfS), particularly regarding direct practice and participatory experiences with children in sustainability contexts (Davis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Davis \u0026amp; Elliott, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; G\u0026uuml;ler Yildiz et al., 2021). There is also a need to investigate emerging local situations, enabling immersion in cultures and interaction with diverse forms of knowledge and definitions present within communities (Bascop\u0026eacute; et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nxumalo \u0026amp; Pacini-Ketchabaw, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Davis and Elliott (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), ECEfS offers opportunities for children to acquire the knowledge, skills, and attitudes needed to identify and address problems relevant to their most significant local contexts. This approach requires a shift toward participatory engagement with children, empowering them to express their opinions and engage responsibly and respectfully with their socio-environmental surroundings, fostering awareness of the daily practices they employ to live (Rodr\u0026iacute;guez-Donoso et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEducation designed for children must provide every child with the opportunity to explore, question, and debate phenomena and connections in their environments, challenging and nurturing their personal interests (Skolverket, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this context, Borg \u0026amp; Samuelsson (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stress the importance of creating participatory learning environments where children act as agents of change, transforming the socio-environmental settings they inhabit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research Question\u003c/h2\u003e \u003cp\u003eConsidering this background, the core research question guiding the study is: How can a learning sequence be designed for early childhood education that enables children in Chile to explore AI through computational thinking while addressing a sustainability problem contextualized in their social, environmental, and cultural surroundings?\u003c/p\u003e \u003cp\u003eTo answer this central question, the study sets out the following aim: To design and validate a learning sequence for early childhood education that enables children in Chile to explore AI through CT while solving a sustainability-related problem relevant to their social, environmental, and cultural context.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Method","content":"\u003cp\u003eThis study follows an educational design research approach (Juuti \u0026amp; Lavonen, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Plomp \u0026amp; Nievee, 2013). This method focuses on teaching activities (e.g., Al\u0026eacute;-Silva \u0026amp; S\u0026aacute;nchez, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huerta-Cancino \u0026amp; Al\u0026eacute;, 2024) and is based on the implementation of flexible design cycles involving iterative and constant implementation, analysis, and redesign. It does not adhere to a specific educational theory (Easterday et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Guisasola Aranzabal et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo achieve the study\u0026rsquo;s objective, we established four design phases:\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Phase 1: Theoretical Review and Identification of an Environmental Problem\u003c/h2\u003e \u003cp\u003eFirst, a theoretical review was conducted to establish foundations for guiding and supporting the design of AI activities based on CT. Various examples of CT activities applied to practical school teaching scenarios were reviewed (e.g., Lindner et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ossovski \u0026amp; Brinkmeier, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Geldreich et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Al\u0026eacute;-Silva, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Araya, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Araya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, educational books on AI were consulted that present practical activities for learning about AI through CT with children (e.g., ReadyAI, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This process made it possible to incorporate design elements used in similar educational materials and research within the field.\u003c/p\u003e \u003cp\u003eSecond, to ensure that the teaching activities were authentic, age-appropriate, and adapted to the region\u0026rsquo;s social and cultural context, various socio-environmental issues affecting Chile and its people were analyzed. After reviewing global reports and several studies on the topic (e.g., IPCC, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Blue Sky, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Morales et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salcedo-Castro et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Christie \u0026amp; C\u0026aacute;rcamo-Ulloa, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Burck et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), environmental problems related to water supply and consumption were selected\u0026mdash;mainly because it is one of the most critical issues currently affecting Chile. This process enabled the incorporation of a local territorial issue related to the care and protection of water bodies and the living beings that inhabit them, which are threatened by pollution from waste and other debris generated by human activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Phase 2: Design of Initial Activity Prototypes\u003c/h2\u003e \u003cp\u003eNext, we conducted multiple discussions and brainstorming sessions to develop initial prototypes of the teaching activities. We created activities resembling class guides for students and tested them internally to assess the dynamics involved.\u003c/p\u003e \u003cp\u003eWe designed several paper-based prototypes to pictorially represent \"categories\" for learning about supervised ML within the local context (e.g., classifying types of waste typically generated by children, types of plastics sold in local markets, types of plants or animals from the area, among similar examples). Finally, drawing inspiration from similar models proposed by ReadyAI (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Lindner et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we decided to create representations for classifying fish as \"endangered\" and \"not endangered.\"\u003c/p\u003e \u003cp\u003eThe next step involved refining the variables displayed in each fish representation, ensuring the minimalistic caricatures were based on the real physical features of fish native to Chile. This proved to be a significant challenge as it required careful attention to details such as colors, markings, and fin shapes. Ultimately, we incorporated the most relevant features of some fish species found in local rivers (e.g., Catfish, Puye, Silverside), though we were unable to include certain variables such as size (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Phase 3: Pedagogical and Didactic Design\u003c/h2\u003e \u003cp\u003eIn this phase, we proposed various dynamics for engaging students in the activities, integrating the previously designed pictorial representations of fish. We tested different ways of analyzing and grouping fish characteristics for classification as \"endangered\" or \"not endangered.\" These efforts combined individual and collaborative teaching strategies, utilizing both free and guided play approaches.\u003c/p\u003e \u003cp\u003eWe developed instructions for educational materials for both teachers and students, including examples and potential answers to the activities to facilitate adaptation to new contexts and scenarios. During this phase, we also defined the approximate durations for each activity and established key evaluation indicators to measure learning objective achievement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Phase 4: Evaluation and Redesign\u003c/h2\u003e \u003cp\u003eFinally, we conducted an evaluation and redesign of the educational activities in two sub-phases.\u003c/p\u003e \u003cp\u003eAs a general approach, we began by evaluating the activities with adults before directly engaging with children, as an ethical precaution to minimize potential risks. Both processes adhered to ethical principles such as transparency, anonymity, confidentiality, and voluntary withdrawal.\u003c/p\u003e \u003cp\u003eFive early childhood education experts evaluated the content and format of the developed materials through a survey (see \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e A), which led to an initial redesign to improve the educational content and structure. The experts, selected for their extensive classroom experience (at least 15 years), independently assessed the activities using a 4-point Likert scale and provided open-ended feedback to justify their choices.\u003c/p\u003e \u003cp\u003eSubsequently, we implemented the activities with 15 children aged 6 to 8 years. The sessions were audio-recorded and supplemented with photographs and field notes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Description of the Analysis Process\u003c/h2\u003e \u003cp\u003eWe analyzed the experts' closed-ended responses using a Likert scale through descriptive analysis techniques across the responses of each evaluator. This included calculating means, standard deviations, and identifying maximum and minimum score values, among other metrics. We also compared response trends to verify their consistency using the statistical indicator \u003cem\u003eKendall\u0026rsquo;s W\u003c/em\u003e (Emerson, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This analysis allowed us to assess the level of agreement among evaluators for ordinal scale data such as the Likert scale. Based on these findings, we proceeded to redesign and improve all educational materials.\u003c/p\u003e \u003cp\u003eThe feedback provided by the experts regarding the activity design, the children's graphical resolution of the experience, the audio recordings, and our field notes were analyzed using inductive content analysis. This type of analysis involves a rigorous and systematic examination of the nature of messages exchanged in communication acts (Krippendorff, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), based on the content that emerges from the data.\u003c/p\u003e \u003cp\u003eTo carry out this analysis, we followed the process described by Rodr\u0026iacute;guez-Donoso \u0026amp; Mauri (2017), which consisted of: (a) an initial reading of the transcriptions, (b) open coding related to the study\u0026rsquo;s research questions, and (c) construction of analytical categories derived from common codes. The data from the activity guide records, audio recordings, and field notes collected during the work with the children were triangulated to visualize relationships and concordances established throughout the experience. Once the information was analyzed, we proceeded with the redesign of the learning experience.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Results of the Validation by Expert Judgment\u003c/h2\u003e \u003cp\u003eRegarding the results of the validation surveys conducted by expert judgment, we observed that, overall, the average trend among all experts was either \"Agree\" or \"Strongly Agree\" that the educational resources were appropriate in terms of format and content (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the format and content of activities by expert.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpert\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFormat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eContent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding the evaluations of each question, we observed that questions related to the format, specifically the clarity in the wording of activity questions (Question 2) and the clarity of graphic resources (Question 3), received the lowest average ratings. In contrast, among the questions related to content, the results for the time allocated for completing the activity (Question 11) received the highest average rating, while the question regarding whether the activities allowed children to understand some basic processes of how ML works, received the lowest average rating (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the format and content of activities by question.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eFormat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e \u003cp\u003eContent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuestion (n\u0026deg;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding content-related questions, the results for \"the time allocated for completing the activities\" (Question 11) received the highest average rating, while the question on whether the activities allowed children to understand some basic processes of how ML works (Question 7) received the lowest average rating. Finally, concerning the agreement observed among experts based on Kendall's W indicator, a moderate and significant agreement was identified for both the format (W\u0026thinsp;=\u0026thinsp;0.536, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and content (W\u0026thinsp;=\u0026thinsp;0.448, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of the proposed educational activities (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from three methods of attribute selection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKendall's W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymptotic significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn relation to the format of the sequence, two categories of agreement among experts were identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFish colors: Experts noted that certain colors make it difficult to see the shapes of the fish and distinguish them (experts 1, 2, and 4). The designs of three fish, in particular, were hard to see due to their very light colors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhotographs of the location: Experts suggested using photographs of Caj\u0026oacute;n del Maipo with polluted areas (experts 1 and 4). Including images relevant to the issue of river waste was deemed pertinent, as this is a primary cause of fish extinction in the area.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eRegarding the content of the didactic sequence, three analytical categories were identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe relationship between the game and AI processes: Experts commented that \"the analogy with AI gets lost during the experience with the fish\" and suggested adding stages of the AI process, providing clear examples of ML, and explaining AI and its characteristics (experts 2, 3, and 4).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe final question of the experience: Experts found it to be too abstract, difficult to represent, and hard for children to understand (experts 1, 2, 3, and 4).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExplaining certain terms and concepts to children: For example, one expert suggested replacing the term \"geographer\" with \"fish researchers\" (experts 1, 2, 4, and 5).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOverall, experts emphasized the importance of ensuring comprehension of basic and fundamental concepts to facilitate children's engagement with the activity. Special attention was given to the relationship between environmental experience and the functioning of AI, as this is the central objective of the activity. Additional suggestions, although not repeated by multiple experts, were considered relevant for redesigning the activity. These included avoiding direct reading and proposing questions related to the characteristics of fish, waste, and their relationship. Some experts suggested replacing terms such as \"teachers\" with \"facilitators\" and \"children\" with \"childhoods.\"\u003c/p\u003e \u003cp\u003eRegarding didactic aspects, expert 1 recommended diversifying strategies since the fish classification activities primarily focused on visual representation. Additionally, she suggested incorporating mediation to help recall the identified variables for constructing the fish classification models. While most of the experts' suggestions were incorporated, not all were included in the initial redesign.\u003c/p\u003e \u003cp\u003eIn relation to the format of the sequence, two categories of agreement among experts were identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFish colors: Some colors were noted to make it difficult to distinguish the shapes of the fish (experts 1, 2, and 4). The designs of three fish, in particular, were highlighted as hard to discern due to their very light colors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhotographs of the location: Experts suggested including photographs of Caj\u0026oacute;n del Maipo with polluted areas (experts 1 and 4). The inclusion of images relevant to the issue of river waste was deemed appropriate, as it is the primary cause of fish extinction in the region.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eRegarding the content of the didactic sequence, three analytical categories emerged:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe relationship between the game and AI processes: Experts noted that \"the analogy with AI is lost during the fish experience\" and recommended including the stages of the AI process, providing clear examples of ML, and explaining AI and its characteristics (experts 2, 3, and 4).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe final question in the experience: Experts described it as too abstract, difficult to represent, and challenging for children to grasp (experts 1, 2, 3, and 4).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExplaining certain terms and concepts to children: For example, one expert suggested replacing the term \"geographer\" with \"fish researchers\" (experts 1, 2, 4, and 5).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOverall, the experts emphasized the importance of ensuring the comprehension of basic and fundamental concepts to facilitate children's engagement with the activity. Special attention was given to the relationship between environmental experience and the functioning of AI, as this is the central objective of the activity.\u003c/p\u003e \u003cp\u003eAdditional suggestions, though not repeated among experts, were considered relevant for redesigning the activity. These included avoiding direct reading and proposing questions related to the characteristics of the fish, waste, and their relationship. Some experts recommended replacing terms like \"teachers\" with \"facilitators\" and \"children\" with \"childhoods.\"\u003c/p\u003e \u003cp\u003eFinally, regarding didactic aspects, expert 1 suggested diversifying strategies, as the fish classification activities mainly focused on visual representation. She also recommended mediation to help children recall the identified variables used to construct the fish classification models. While most of the experts' suggestions were incorporated, not all were included in the initial redesign.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Implementation with Children\u003c/h2\u003e \u003cp\u003eThe learning experience was implemented with 15 children aged 6 to 8 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the main results were organized according to the activity stages:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the introductory stage, we observed significant enthusiasm for starting the activity, especially after presenting a description of how machine training works. At this stage, questions such as, \"Do we have to read all of this?\" were raised, referring to the sheets held by the facilitator. The presentation of the geographical map proved crucial for situating the environmental problem within the children's local context, as did the images of rivers with and without waste.\u003c/p\u003e \u003cp\u003eAs the experience progressed, the children's initial reaction to observing endangered fish was to group them according to their own observations and classification criteria, often based on fish colors and whiskers. After mediation, the children grouped fish characteristics effortlessly, focusing on features like eyes, tails, dorsal and pectoral fins, and whiskers. Numerical concepts were also introduced during mediation, such as \"half of them have whiskers\" or \"half of them have spots.\" When asked if they wanted to name any other features, one child noted that the colors of the fish's bellies also matched\u0026mdash;a detail none of the experts had identified.\u003c/p\u003e \u003cp\u003eThe second stage, which involved classifying non-endangered fish, was somewhat quicker. However, younger children (ages 6 and 7) exhibited some anxiety to finish the game quickly. When invited to verify the models they had created in their drawings, their interest and curiosity were reignited. Some children immediately began pointing out which fish were \"endangered\" without necessarily grouping them. The facilitator suggested they observe the fish calmly and in detail to carry out the classification properly. Most children subsequently evaluated the likelihood of fish being endangered or not with remarkable accuracy and were very pleased with their achievement.\u003c/p\u003e \u003cp\u003eIn the final stage, during the conclusion of the experience, one of the initial responses to the application question was: \"We need to look at the fish that are endangered because if there are endangered fish, it\u0026rsquo;s because there\u0026rsquo;s trash.\" They also noted that it\u0026rsquo;s important to observe the water's color and whether there\u0026rsquo;s oil, which is also considered trash. Another suggestion was to check if river animals were trapped by something, as their lack of movement could indicate they were caught in waste.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Final Design\u003c/h2\u003e \u003cp\u003eAfter integrating the results from all evaluations, we have developed the final version of the educational design. The Play is available as an open-access resource on the website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jhonalesilva.github.io/AI-Fish/\u003c/span\u003e\u003cspan address=\"https://jhonalesilva.github.io/AI-Fish/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The aim of this educational design is: \"To recognize some of the basic processes of supervised ML through classification, applied to a sustainability problem for children aged 6 to 8 in Chile.\" The full implementation of the design takes approximately 45\u0026ndash;60 minutes and is divided into five secondary activities, all aligned with the supervised machine learning (ML) process and the framework proposed by APEC InMside (Isoda et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e): (1) Training with labels, (2) Modeling, (3) Prediction and verification.\u003c/p\u003e \u003cp\u003eThe first activity focuses on simulating \u0026ldquo;training with labels.\u0026rdquo; At this stage, children perform groupings, comparisons, and drawings to classify common characteristics of endangered fish. They create drawings on a template that includes predefined \"categories\" or \"labels\" for grouping variables to identify the primary morphological traits of endangered fish (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe second activity involves contextualization. Children take on the role of \"fish researchers in the Caj\u0026oacute;n del Maipo River,\" identifying some habitat characteristics of endangered fish and reflecting on the problem of river waste (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the third activity, the \"training with labels\" simulation is repeated. This time, children group the common characteristics of fish that are not endangered. They then draw each of the shared characteristics (labels) that most of these fish have in common (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe fourth activity is a guessing game. Children observe a new set of images of unknown fish. They attempt to predict whether the fish are endangered using the templates they previously created, replicating the \"model testing\" stage of supervised ML. At the end of this activity, children compare their predictions and explain their reasoning to their peers (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the final activity, children connect corresponding \"characteristics\" to each \"unknown fish\" with lines to determine, with greater confidence and based on observed trends, whether the fish is endangered (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe activities conclude with an explanation of the main stages of supervised learning, comparing them to the activities the children completed, and reflecting on other applications for environmental conservation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion and Conclusions","content":"\u003cp\u003eAI, climate change, and sustainability have become central topics of global interest (United Nations, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UN, 2023). However, these subjects remain challenging to address in early childhood education. While the future is inherently unpredictable, keeping student curricula updated with trends such as AI and sustainability seems crucial for adapting, thriving, and shaping what lies ahead. Recent literature reviews highlight this need, increasingly showing that combining AI with active teaching strategies and methodologies enhances students' development of 21st-century skills (e.g., Gerlich, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ni\u0026ntilde;o et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Celik et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is also critical for mitigating concerns associated with AI use, such as cognitive passivity or inhibition of critical thinking practices.\u003c/p\u003e \u003cp\u003eAI, climate change, and sustainability have become central topics of global interest (United Nations, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UN, 2023). However, these topics remain challenging to address in early childhood education. While the future is inherently unpredictable, keeping student curricula updated with trends such as AI and sustainability appears crucial to adapt, thrive, and shape what lies ahead. Recent literature reviews highlight this need, increasingly showing that combining AI with active teaching strategies and methodologies enhances the development of 21st-century skills in students (e.g., Gerlich, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ni\u0026ntilde;o et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Celik et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is also essential to mitigate concerns associated with AI use, such as cognitive passivity or the inhibition of critical thinking. At the same time, establishing a direct link between AI and sustainability from an early age would allow for a better understanding of the environmental implications and ethical limitations associated with how AI works\u0026mdash;thereby increasing public trust and helping to reduce fears tied to its use.\u003c/p\u003e \u003cp\u003eFollowing recommendations from previous studies and major international frameworks (e.g., Isoda et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Miao \u0026amp; Shiohira, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Miao \u0026amp; Cukurova, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we set out to integrate knowledge from Computer Science, Education, and Environmental Sciences (Heeg \u0026amp; Avraamidou, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yeter et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Deng \u0026amp; Yu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kit et al., 2022) to design educational resources aimed at students that are tangible, accessible, low-cost, and culturally relevant, in order to address an environmental issue rooted in the national context. This approach to developing tangible resources is also consistent with Yi et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who emphasized the importance of using AI technologies adapted to early childhood education contexts, such as interactive robots, which allow children to explore and understand basic AI principles interactively using concrete materials before progressing to more abstract processes. Similarly, Su \u0026amp; Zhong (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also highlighted the importance of using tangible educational resources and programmable artifacts to foster social interaction and help young children understand AI principles.\u003c/p\u003e \u003cp\u003eAmong the findings, this study identifies that challenges and difficulties still remain for young children to deeply understand core ML concepts through CT. However, progress in this area is also evident. The results suggest that, despite persistent difficulties, children gradually become familiar with basic notions of statistical and CT related to supervised ML. From their interaction with the designed games, spontaneous and natural notions emerge related to numerical quantities, whole numbers, fractions, and even the concept of probability. While children enjoy learning about the fish that inhabit their local area, they use these mathematical concepts to classify, recognize patterns, and ultimately solve a problem that requires applying prior experience\u0026mdash;an approach that aligns with the process of supervised ML. Additionally, it is important to note that these activities were designed, evaluated, and improved by educators, and they addressed a socio-environmental issue tied to the local context and culture. This latter point makes the designed activities more meaningful for learning by connecting with children\u0026rsquo;s prior knowledge and experiences, which also aligns with Yang\u0026rsquo;s (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) ideas on the need to promote a culturally responsive pedagogy that allows children to meaningfully explore AI technologies.\u003c/p\u003e \u003cp\u003eOn the other hand, to date, there are few studies that address the design of educational resources for teaching AI and sustainability to young children. One of the few cases we found was, for example, the study by Araya et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which proposed addressing problem-solving related to pandemics, contagion, and data analysis through CT. This lack of empirical studies has resulted in a limited availability of materials for teachers to implement these topics in the classroom and for curriculum designers to develop adequate teaching guidelines. This is consistent with the conclusions of Su \u0026amp; Yang (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who identified that achieving deep learning through CT remains a pending challenge, and that more and newer educational resources and activities appropriate for its teaching are needed\u0026mdash;including assessment instruments and criteria to guide the selection of learning tools. The scoping review conducted by Su et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also highlights this absence in curriculum design and teaching guidelines, which poses challenges for educators seeking to integrate AI literacy in early childhood education. This point supports the urgent need to develop resources and pedagogical guidelines that facilitate the integration of AI and sustainability into early education levels.\u003c/p\u003e \u003cp\u003eAs in the findings by Bell et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Lindner et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), this study demonstrated progress in how to create experiences that help children understand central ideas of AI and ML through CT, with the difference that it emphasized the integration of environmental disciplines and problems related to the water crisis affecting Chile. This focus could help promote the kind of critical digital literacy proposed in the international framework developed by UNESCO, as elaborated by Miao \u0026amp; Shiohira (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Miao \u0026amp; Cukurova (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where the goal is to prepare both students and teachers not just to use AI, but also to develop a deep and critical understanding of its social and environmental impacts, emphasizing its ethical and responsible use. Parallels were also found with the framework of Labanda-Jaramillo et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Sanusi et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which promotes the teaching and learning of AI in K-12 education through CT-based activities.\u003c/p\u003e \u003cp\u003eFinally, based on the evaluations and results obtained, it is concluded that the process of designing and validating a learning sequence to teach AI and sustainability in early childhood education in Chile was a successful first step. This sequence allows children to learn and become familiar with cross-cutting concepts of supervised ML through CT, statistical thinking, play, and problem-solving related to sustainability, all adapted to their social, environmental, and cultural context.\u003c/p\u003e"},{"header":"6. Limitations and Future Work","content":"\u003cp\u003eThis study presents a sampling limitation regarding the number of children who participated in the implementation of the designed activity sequence. Therefore, it is important to consider that the evidence included in this study may have been influenced by the small sample size. In light of this, it becomes relevant for future research to also explore the integration of AI and sustainability, but with larger student samples and with evaluation strategies and instruments for CT validated for early childhood, such as those proposed by Xiang et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which enable new assessments based on classroom interaction observations.\u003c/p\u003e \u003cp\u003eAt the same time, it is important to note that this study did not consider the participation of other key agents in the teaching process of young children. Therefore, from an ecological perspective, future research should also include the involvement of teachers and families in both the design and evaluation processes of teaching resources, as teachers and families are the individuals most actively involved in children's lives.\u003c/p\u003e \u003cp\u003eOn the other hand, although some limitations still exist regarding students\u0026rsquo; difficulties in conceptualizing the stages of AI, we hope that the strategies and combinations used in our educational design may serve as a model to guide future empirical research. Such research could deepen and propose more and new teaching activities aimed at addressing this challenge. This point should also be reflected in future studies that explore new social and environmental issues relevant to teaching and school curricula, while also addressing other ethical and legal implications related to the regulation and scope of these technologies, in order to foster trust from an early age.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to the need to protect and preserve respondents’ confidentiality. However, they are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was reviewed and approved by the University of Chile. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study. Written informed consent was obtained from the parents or legal guardians of the child participants. Additionally, assent was obtained from the child participants prior to their involvement in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that human research participants provided informed consent and assent for publication of the data collected during the study. Parents or legal guardians of the child participants signed informed consent regarding publishing their children's data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.A.; methodology, J.A. and M.R.-D. formal analysis, J.A.; investigation, J.A. and M.R.-D.; data curation, J.A.; writing—original draft preparation, J.A.; writing—review and editing, J.A.; supervision, J.A.; project administration, J.A.; funding acquisition, J.A. and M.R.-D. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been developed with the support of ANID BECAS/DOCTORADO NACIONAL 21240783 and ANID BECAS/DOCTORADO NACIONAL 21241617.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl\u0026eacute;, J., \u0026amp; Arancibia, M. L. (2025). Emerging Technology-Based Motivational Strategies: A Systematic Review with Meta-Analysis. 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Discover Education, \u003cem\u003e1\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44217-022-00015-w\u003c/span\u003e\u003cspan address=\"10.1007/s44217-022-00015-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence in Education, Early Childhood Education, Sustainability, Computational Thinking.","lastPublishedDoi":"10.21203/rs.3.rs-5954038/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5954038/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn line with the principles of peace, justice, and sustainability, Artificial Intelligence (AI) and its subfields, such as Machine Learning, have the potential to make learning more equitable, accessible, and inclusive. To achieve this, it is important to promote educational experiences from early ages that help children understand how AI works, using low-cost, easily accessible resources that are contextualized to real-world problems. This study seeks to contribute to that goal in an underexplored age group by presenting the design of a sequence of learning activities for early childhood education. The sequence combines strategies based on Computational Thinking (CT) and play, focusing on the care of endangered fish species in Chile. Methodologically, a four-stage design-based research approach was followed, including two rounds of evaluation and redesign: first, five experts assessed the content and format of the proposed activities through a closed-question survey and provided open-ended feedback on their choices; then, a second evaluation was carried out in which the activities were implemented with 15 children aged 6 to 8, and the sessions were recorded and photographed. Descriptive, statistical, and content analyses were conducted on the collected data. Overall, the results indicate that the experts positively validated the educational resources in terms of format and content, while also identifying difficulties related to children\u0026rsquo;s understanding of AI. Across the board, the implementation with children revealed a strong interest in the environmental issue and in its combination with CT-based activities.\u003c/p\u003e","manuscriptTitle":"Education for Sustainability and Artificial Intelligence based on Computational thinking: A Nature Care Play for Early Childhood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 09:57:55","doi":"10.21203/rs.3.rs-5954038/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T09:19:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T18:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141942864310653073191870090298043849914","date":"2025-09-01T02:06:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301708492179473694665961152131972279974","date":"2025-08-29T12:53:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251797738424223227971593342179765249652","date":"2025-08-28T19:21:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69796833982633970707257217899700007867","date":"2025-08-26T07:38:53+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T14:11:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T21:08:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53858502674325012547326262905286102242","date":"2025-04-27T20:37:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-25T15:18:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T10:44:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2025-04-15T22:47:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51f599ff-6994-40fc-a0fa-dced2909a95c","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T16:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 09:57:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5954038","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5954038","identity":"rs-5954038","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}