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Special Session: AI for K-12 Guidelines Initiative
Abstract
In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching K-12 students about artificial intelligence. Inspired by CSTA's national standards for K-12 computing education, the - AI for K-12 guidelines (ai4k12.org) will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The working group is also creating an online resource directory where teachers can find AI-related videos, demo software, and activity descriptions they can incorporate into their lesson plans. The goal of this session is to raise the SIGCSE community's awareness of the initiative, its deliverables, and outcomes, and to foster a community-wide conversation about AI education in K-12. This initiative parallels other recent initiatives in K-12 AI education and community-wide initiatives and discussions around CS For All and CS in K-12. This Special Session is aimed toward K-12 CS educators, researchers, and curriculum and tool designers.
... • The AI4K12 Initiative and The Big Ideas in AI [17,44]. ...
... AI4K12 Initiative [44]. Later, the framework was further developed based on knowledge from best practice [17]. ...
... The AI literacy for technology education framework is based on policy documents, empirical research in terms of interviews with teachers, experts in the field of AI literacy, and a literature review [13,[16][17][18]44,45]. In merging those materials, we argue that this framework stands on solid ground. ...
The interest in artificial intelligence (AI) in education has erupted during the last few years, primarily due to technological advances in AI. It is therefore argued that students should learn about AI, although it is debated exactly how it should be applied in education. AI literacy has been suggested as a way of defining competencies for students to acquire to meet a future everyday- and working life with AI. This study argues that researchers and educators need a framework for integrating AI literacy into technological literacy, where the latter is viewed as a multiliteracy. This study thus aims to critically analyse and discuss different components of AI literacy found in the literature in relation to technological literacy. The data consists of five AI literacy frameworks related to three traditions of technological knowledge: technical skills, technological scientific knowledge, and socio-ethical technical understanding. The results show that AI literacy for technology education emphasises technological scientific knowledge (e.g., knowledge about what AI is, how to recognise AI, and systems thinking) and socio-ethical technical understanding (e.g., AI ethics and the role of humans in AI). Technical skills such as programming competencies also appear but are less emphasised. Implications for technology education are also discussed, and a framework for AI literacy for technology education is suggested.
... Another notable initiative, AI4K12, was launched by researchers and experts from the USA (Touretzky et al., 2019). The project had three main objectives: i) adopting a national guideline for teaching AI; ii) creating an online repository of AI educational resources; and iii) forming a group of scholars and practitioners in education and computer science to improve AI teaching in pre-tertiary education. ...
... The curriculum begins with the definition of AI and its various applications, then introduces three key modules: i) how computers communicate: designing interfaces; ii) how computers see: computer vision; and iii) how computers listen: audio applications. All modules align with the five big ideas outlined by Touretzky et al. (2019), reflecting the diverse aspects of AI. ...
With the proliferation of artificial intelligence (AI) across various global industries, it has become necessary to introduce AI education in pre-tertiary curricula. This topic has started to gain serious attention in many countries, with numerous real-world initiatives emerging. However, in developing countries, notably Morocco, this topic is rarely addressed in the literature, and relevant initiatives are virtually nonexistent, to our knowledge. This paper aims to fill this gap by presenting a perspective study on the integration of AI into the computer science (CS) curriculum in Moroccan high schools. Specifically, the paper i) highlights international initiatives in AI education as well as the current state of AI education in Morocco; ii) evaluates the current CS curriculum in Morocco, emphasizing its weaknesses and calling for a comprehensive review that incorporates AI teaching; iii) argues for the integration of AI into the high-school CS curriculum; iv) recommends and discusses specific approaches for stakeholders in the education field to consider; v) explores critical challenges and considerations that could hinder this; and vi) provides practical tools and resources to facilitate AI education in Moroccan schools.
... At the outset, several international [3], [6], [10] and national [4] policy documents, as well as existing AI PD programs [11], were analyzed to determine the minimum learning outcomes for the target group-K-12 teachers. These learning outcomes were then presented for discussion to a diverse group, which included researchers, teachers, and teacher trainers from various disciplines, such as engineering education, vocational education, computer science, mathematics, and others. ...
... [6]. The first module provides a concise theoretical introduction to AI, offering teachers an overview of fundamental concepts from AI and machine learning (ML) as covered in relevant literature [6], [10]. This includes an explanation of large language models (LLMs) like ChatGPT-4, helping teachers understand the underlying mechanisms behind these technologies. ...
In Germany, professional development (PD) programs are primarily organized by federal institutes, which have the resources to scale these programs, while universities face challenges such as limited long-term funding and staff turnover. Teacher trainers at federal institutes, typically subject matter experts (e.g., in mathematics), often lack AI knowledge and have expressed a need for AI-focused training. While universities tend to develop PD programs independently, there is little collaboration with federal initiatives. To bridge this gap, a collaborative project was initiated to create AI-related PD materials in partnership with teacher trainers. These materials are designed for piloting and dissemination by trainers and target teachers across multiple subjects, including vocational educators in fields like electrical engineering. This work-in-progress paper describes the development of a PD course structured around a Hybrid Learning Landscape (HLL) centred on AI. The HLL supports teachers in lesson planning, task differentiation, and the integration of AI tools, while providing foundational AI knowledge. The HLL content focuses on five core areas: (1) basic AI knowledge, (2) AI tools, (3) task differentiation, (4) quiz generation, and (5) additional AI-related PD resources. The preliminary findings on the collaboration align with related work and highlight the need to navigate the varying logics, interests, and expectations of the stakeholders involved. Networking meetings and exchange of ideas as well as continuous support seem relevant for a sustainable approach to AI PD.
... Lee conducted a study of AI education in the primary and secondary schools [7]. The study indicated that over the last two years, Korea and the United States (AI4K12) proposed national curriculum standards for schools to design their curriculum and guidelines and policies such as teacher professional programs [8], [9]. It also reported that the European Union utilized courses and resources online to nurture population-wide AI literacy, rather than designating students or subjects at specific school levels. ...
... Moreover, this finding showed that the co-creation process effectively created synergy among the AI and education experts and secondary school teachers to create the curriculum, which includes the components of overview, framework, content, and activities. This process aligns with the development approach of the recent studies done in Australia, mainland China, Korea, India, the United States, and Turkey [8]- [10], [15], [16]. Accordingly, designbased research is an appropriate methodology to bridge the gap between researchers and practitioners in developing AI education [28], [29]. ...
Abstract—Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created the first pre-tertiary AI curriculum at the secondary school level for Hong Kong and evaluated its efficacy. This study added to the AI education community by introducing a new AI curriculum framework. The pre-posttest multi-factors evaluation about students’ perceptions of AI learning confirmed that the curriculum is effective in promoting AI learning. The teachers also confirmed the co-creation process enhanced their capacity to implement AI education.
Background: AI4Future is a cross-sector project that engages five major partners – CUHK’s Faculty of Engineering and Faculty of Education, secondary schools, Hong Kong government and AI industry. A team of 14 professors collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum.
Research Questions: Would the curriculum significantly improve the student perceived competence, attitude and motivation toward AI learning? How does the co-creation process benefit the implementation of curriculum?
Methodology: The participants were 335 students and 8 teachers from the secondary schools. This study adopted a mix-method with quantitative data measures at pre- and post- questionnaires and qualitative data emphasizes teachers’ perspectives on the co-creation process. Paired t-tests and ANCOVAs, and thematic analysis were used to analyze the data.
Findings: 1) students perceived greater competence, and developed more positive attitude to learn AI, and 2) the co-creation process enhanced teachers’ knowledge in AI, as well as fostered teachers’ autonomy in bringing the subject matter into their classrooms.
... Artificial intelligence (AI) plays a vital role in the constant increase of societal digitization. Thus, major countries all over the world put a lot of effort into providing high-quality teaching in the field of AI by using AI and technology in teaching (Touretzky et al., 2019). AI-supported technology has become increasingly crucial in our daily lives; as it changes our way of thinking, behaviors, and interactions with one another (Chen et al., 2020a). ...
Citation: Abualrob, M. M. (2025). Innovative teaching: How pre-service teachers use artificial intelligence to teach science to fourth graders. Contemporary Educational Technology, 17(1), ep547. https://doi. This study aims to uncover the prompts most frequently repeated by pre-service teachers when using the Copilot technique, as well as their reflections on its use in preparing and planning science lessons for fourth graders. The qualitative research methodology with an exploratory case-study design was conducted on a purposeful sample of 20 pre-service teachers. The sample was divided into four focus groups. Data was collected through document analysis of the outcomes from the pre-service teachers' artificial intelligence creations, their reflective journal entries, and the discussion that occurred during the four focus groups' interviews. The study's results revealed that the applications mostly used by pre-service teachers include lesson plans, instructional media, authentic assessment, tables, pictures, drawings, and instructional strategies. Six themes emerged from the reflective Journal and focus groups' interview analysis connected to the use of the Copilot method in teaching. These themes were the following: developing cognition of new ideas, attracting attention to things that never crossed their minds, saving time and effort, compatibility with students' needs, less human interaction, and dependency.
... 62 papers were discounted (31.47%), as they were less than 3 pages in length. There was some interesting preliminary work in some of these shorter papers [17,28,29,38] but they were all without findings. The remaining 102 results (51.78%) were discussed in the context of unrelated fields, or included older students. ...
This paper presents the results of a systematic review of the literature relating to artificial intelligence (AI) and machine learning (ML) education at school level. We conducted a search of the ACM Full-text Collection and 33 papers from the 197 search results were selected for analysis. In this context, we considered the research questions: 1) Who has been the focus of the research?, 2) What course content appears in the research?, and 3) Where has the research taken place? We find that there has been a recent marked increase in research on AI/ML for school level education, although most of this has been based in the United States. The majority of this research focuses on students, with very little specifically addressing teachers, experts, parents, or the wider school community. There is also a lack of attention paid to research focused on women or those from historically underrepresented groups and equity of access to AI/ML courses for school-level students. Finally, the content covered in the courses described in this research varies widely, possibly because there is so little alignment to computer science (CS) frameworks or curricula.
... In the third block, they learned how to analyze the didactic value of the unplugged activities using Cody & Roby, 2 a game in which Cody (i.e., the programmer) creates instructions that Roby (i.e., the robot) must execute. The final block was dedicated to the presentation of unplugged activities for the teaching of Artificial Intelligence (Touretzky et al., 2019). In total, the teachers dedicated 30 h to complete all of these training blocks. ...
Computational thinking (CT) is a multidimensional term that encompasses a wide variety of problem-solving skills related to the field of computer science. Unfortunately, standardized, valid, and reliable methods to assess CT skills in preschool children are lacking, compromising the reliability of the results reported in CT interventions. To surpass this limitation, we validated in a sample of 700 preschool students (5–6 years old) the Beginners Computational Thinking test Short-Form (BCTt-SF), an unplugged 12-item instrument that measures three of the most common computational concepts assessed in preschool research: sequences, loops, and conditionals. The theoretical model underpinning the BCTt-SF was supported by dimensionality assessment, which suggested that preschool students can be distinguished in terms of four specific abilities (i.e., sequences, simple loops, nested loops, and conditionals) and that all of these abilities were related by a general factor. We modeled this hierarchical structure with a bi-factor model that presented excellent psychometric properties, from good statistical fit indices to adequate reliability of the general ability. To take full advantage of this model, we created an online application in the Shiny platform (https://computationalthinkingtests.shinyapps.io/SF-BCTt/) for the seamless scoring of examinees by any teacher or researcher who uses the BCTt-SF to assess CT skills in preschool children. Finally, we demonstrated how the BCTt-SF can be used to test the impact of educational interventions for improving CT skills in preschoolers.
... Such hands-on experiences help bridge the gap between theoretical knowledge and real-world applications, providing students with practical insights into AI (Visvizi, 2021). Initiatives such as the AI4K12 guidelines, which outline the five big ideas of AI for K-12 education and can be adapted for higher education, emphasize the importance of understanding AI concepts, including perception, representation and reasoning, learning, natural interaction, and societal impact (Touretzky et al., 2019). ...
This study explored university students’ perceptions of artificial intelligence (AI) literacy and AI education. Specifically, the authors sought to understand the level of AI literacy among university students, the extent of their prior exposure to AI education, and the factors that affect their AI education. The study was conducted through an online survey distributed among 300 university students at a Korean university. The study findings indicate that most university students are interested in learning AI. Moreover, the results showed that students with prior experience with software (SW) education showed superior knowledge, a better grasp of AI concepts, and more confidence in using AI technologies, especially in computer coding skills. On the other hand, students with less exposure to prior AI education expressed a need for more learning opportunities and sufficient knowledge of computer coding skills. In addition, we found that students who had received SW education during their K-12 schooling showed better math skills than those who had only received short-term training in university. These findings underscore the importance of addressing the educational barriers that impede university students’ ability to harness this interest effectively. In conclusion, our study provides valuable insights into university students’ perceptions regarding AI literacy and the factors that affect their AI education.
... Recognizing its potential, countries worldwide, including the United States, China, and the United Kingdom, are formulating national policies to integrate AI into their educational systems. First, in the United States, AI education utilizing machine learning is emphasized, and the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) have formed and are operating a collaborative body called the 'AI4K12 Initiative' [4]. Through this collaborative body, they develop and research national guidelines for AI education for K-12, standard curricula, and teacher training programs. ...
As artificial intelligence (AI) and data science education gain importance in K-12 curricula, there is a growing need for well-designed sustainable educational datasets tailored to different school levels. Sustainable datasets should be reusable, adaptable, and accessible to support long-term AI and data science education goals. However, research on the systematic categorization of difficulty levels in educational datasets is limited. This study aims to address this gap by developing a framework for sustainable educational dataset standards based on learners’ developmental stages and data preprocessing requirements. The proposed framework consists of five levels: Level 1 (grades 1–4), where data preprocessing is unnecessary; Level 2 (grades 5–6), involving basic data cleaning; Level 3 (grades 7–9), requiring attribute manipulation; Level 4 (grades 10–12), involving feature merging and advanced preprocessing; and Level 5 (teachers/adults), requiring the entire data science process. An expert validity survey was conducted with 22 elementary and secondary school teachers holding advanced degrees in AI education. The results showed high validity for Levels 1–4 but relatively lower validity for Level 5, suggesting the need for separate training and resources for teachers. Based on the CVR results and expert feedback, the standards for Educational Datasets were revised, particularly for Stage 5, which targets teachers and adult learners. The findings highlight the importance of expert validation, step-by-step experiences, and an interdisciplinary approach in developing educational datasets. This study contributes to the theoretical understanding of educational datasets and provides practical implications for teachers, students, educational institutions, and policymakers in implementing effective and sustainable AI and data science education in K-12 settings, ultimately fostering a more sustainable future.
... The learning design for our CSCL environment is informed by constructionism, which suggests that students learn through constructing sharable and personally meaningful artifacts (Kafai, 2006;Papert, 1980), and the perspective that students learn AI/ML through participation in socio-technical practices (Lave, 2011). Among the limited number of published research on K-12 AI/ML education (e.g., Gresse von Wangenheim et al., 2021;Touretzky et al., 2019;Zhang et al., 2022), there have been some CSCL environments studied, in the form of group projects to create ML models with computer-supported AI/ML tools. Mariescu-Istodor and Jormanainen (2019) implemented a ML lesson for high schoolers to work in groups on image recognition tasks with designed tool. ...
To address the pressing need of AI and Machine Learning (ML) education at K-12 level, we designed and implemented an online curriculum that engaged middle school students in ML projects. Ten students from Grade 6-8 participated in a 16-hour online weekend program in Spring 2023. With ML computer tools, students collected data, trained and tested their own ML models, and shared and discussed ML products with each other. This study investigates how the designed constructionist, CSCL environment supported students' learning in ML practices. We used interaction analysis methods to analyze video recording episodes of students' collaborative learning in k-means clustering projects. Our findings show that students developed understandings of the clustering mechanism and labeling practices through iterations of collaborative observing, hypothesis making, investigating, and problem-solving. The study sheds light on the design of CSCL environment for ML education.
... In the last years, an ever-growing number of curricular initiatives (e.g., [15,36]) as well as teaching concepts, materials, and tools for the classroom [5,27,41] for AI in K-12 education were developed. However, to successfully incorporate this new topic in the classroom, teachers must also be qualified. ...
... One representative project is AI for K-12 (AI4K12). The AI4K12 organisation presented a framework called Five Big Ideas (see Figure 11.1) which encompasses various aspects of AI, namely (1) Perception, (2) Representation and Reasoning, (3) Learning, (4) Natural Interaction, and (5) Societal Impact (Touretzky, Gardner-McCune, Breazeal, Martin, & Seehorn, 2019a;Touretzky, Martin, Seehorn, Breazeal, & Posner, 2019b). A growing body of research (e.g., Nisheva-Pavlova, 2022;Su & Zhong, 2022) acknowledges that AI4K12's Five Big Ideas framework makes AI education more accessible for K-12 teachers. ...
Artificial Intelligence (AI) education is a rapidly growing research area and a critical addition to K-12 education. However, little research has investigated how AI education can be taught at the K-12 level. As a result, teachers have lacked clarity on what they should do to incorporate AI, and they need additional scaffolding. This chapter first reviews a collection of K-12 AI education literature and identifies the essential knowledge and core competencies based on existing practices. We then report on a case study conducted at a Hong Kong primary school. In this study, we organised co-design workshops with the STEM teachers and explored how they can integrate AI education into their existing STEM education curriculum. Lesson Study has been widely used as a participatory teacher capacity-building model for advancing TPACK – an overarching set of skills needed for facilitating effective technology-based lessons. During Lesson Study, teachers work under the facilitation of the researcher to co-created lesson plans where AI essential knowledge and core competencies identified were embedded. The lesson plans were implemented to demonstrate if teachers could successfully apply the pedagogies learned in the workshops. Qualitative evaluation reveals that the Lesson Study were generally effective in raising teachers’ competency in integrative, learning-by-doing, and project-based AI lessons. This success suggests that Lesson Study with knowledgeable others could serve as entry points for Hong Kong K-12 schools incorporating AI education. We reflect on the co-designing experiences with the teachers and synthesise them into key “lessons learned” for K-12 AI education. Opportunities, challenges and issues, and future directions are discussed.
... Arguably the most well-known initiative regarding AI education is described by Touretzky et al. [69] where a joint working group was formed from the Association for the Advancement of AI and the CSTA develop guidelines to teach AI in K-12 in the US, the initiative AI4K12 (https://ai4k12.org/) was emerged. ...
One of the most researched domains of computing education research (CER) that attracts attention is computing education in schools, starting from pre-primary level up to upper secondary level (K-12). A high number of initiatives and related research contributions have appeared over half a century of computing history in schools. This chapter presents an overview of CER in the K-12 domain, including globally influential movements such as that of Logo pedagogy, constructionism, inquiry based learning or computational thinking (CT). Development of CT in education, based on a number of previous reviews on CT and K-12, paints a diverse picture of the approaches, educational technologies, pedagogical innovations, and related challenges such as lack of teacher training or shortage of learning resources. This article presents also a scientometric overview of CER research in the K-12 domain. The analysis identifies the top topics of research, and foundational articles. While much of the research is centered around the US, key research from other parts of the globe is also highlighted. Emergence of new trends such as teaching artificial intelligence and machine learning in schools are also discussed.
... El curso finalizó con un bloque dedicado a la Inteligencia Artificial, que presenta el marco de enseñanza de la Inteligencia Artificial propuesto por AI4K12 2 (Touretzky, Martin, Seehorn, Breazeal, Posner, 2019) y propone actividades desenchufadas para familiarizarse con algunos de los conceptos fundamentales de este campo. ...
La Escuela de Pensamiento Computacional e Inteligencia Artificial (EPCIA) es un proyecto del Ministerio de Educación y Formación Profesional, que se desarrolla en colaboración con las Consejerías y Departamentos de Educación de las comunidades y ciudades autónomas. El objetivo del proyecto es ofrecer recursos educativos abiertos, formación, acompañamiento y evidencias de impacto en las prácticas educativas y en el aprendizaje del alumnado, a fin de impulsar la incorporación del pensamiento computacional en la práctica docente a través de actividades de programación y robótica. Este proyecto, que está dirigido a docentes de todas las etapas educativas no universitarias y de cualquier materia o especialidad, lanzó su primera edición en el curso 18/19 en la que se inscribieron más de 700 docentes y durante el curso 19/20 en la que se inscribieron más de un millar de docentes de la práctica totalidad del país para participar en el proyecto. En este caso, la temática se centró en la Inteligencia Artificial. Uno de los objetivos de este proyecto es que la formación de los docentes se traslade a las aulas. Por ello, las tareas prácticas con las que el profesorado participante se familiarizó durante la fase de formación estaban diseñadas para ser utilizadas directamente en el aula. De este modo, los docentes de esta edición de la EPCIA han llevado a la práctica, con su alumnado, al menos 5 sesiones de trabajo relacionado con el pensamiento computacional y la Inteligencia Artificial. Por último, y en paralelo con la Fase 2, de puesta en práctica, se realizó una investigación para medir el impacto del proyecto en el aprendizaje y en la práctica docente. Esta investigación se ha desarrollado de forma independiente, pero coordinada, en las tres propuestas de la EPCIA: las actividades desconectadas, la programación con bloques (Scratch) y el desarrollo de apps con App Inventor, estas dos últimas combinadas con Machine Learning for Kids. Son los resultados de esta investigación los que se presentan en este informe.
... The guidelines unpack AI's "Big Ideas" along five thematic strands, and are organized by four grade bands: K-2, 3-5, 6-8, and 9-12. The working group is also creating an online resource directory where teachers can find AI-related videos, demo software, and activity descriptions they can incorporate into their lesson plans [25]. ...
Artificial Intelligence (AI) is intervening positively in educations. UNESCO considers as a new vision to involve AI not only as a didactic medium but also as a science in which children can develop their intellect, through workshops, courses, and curricula focused on the fundamentals of AI, allowing them to develop skills such as computational and critical thinking. This research aims to design the DIA4K12 framework, which proposes the structure to support the teaching-learning process of AI in primary and secondary education. The core of the framework consists of four phases: planning, execution, process, and development; three components: open educational resources, K-12 curriculum and active methodologies; five sublevels: logical reasoning, computational thinking, and disconnected artificial intelligence, mathematics for AI, programming and machine learning; and three transversal axes: communities (communities of practice), open license (creative commons) and ethics. Finally, the framework was applied to a case study in the context of the Ecuadorian General Basic Education curriculum for the subject of Mathematics, using three phases, three components, and two sublevels.
KeywordsEducationSchool curriculumMachine learningComputational thinkingInformation technology education
... Education aims to improve students' skills needed by society and prepare them for the future. Therefore, basic thinking skills are very important, especially in using artificial intelligence (AI) (Touretzky et al., 2019). It should be noted that well-known industrialized countries, such as China, South Korea, and the United States, are preparing and planning for the future of their AI-based education systems and patterns (Shin, 2019). ...
This research aimed at utilizing artificial intelligence in STEM-based creative learning in the society 5.0 era. The researchers investigated how an educator can utilize artificial intelligence and optimize it into a STEM-based learning process. STEM stands for Science, Technology, Engineering, and Math. The United States initiated it to combine the four disciplines integrated into a problem-based learning method and everyday contextual events. Artificial intelligence is an intelligence added to a system managed in a scientific context. Artificial intelligence is created and put into a machine (computer) to do work like humans. Several fields that use artificial intelligence include expert systems, computer games (games), fuzzy logic, artificial neural networks, and robotics. The researchers employed the literature review or library research by reviewing the results of various studies and collecting data from assorted references and sources. In conclusion, implementing artificial intelligence in STEM-based creative learning can be an alternative for an educator in the learning process. Artificial intelligence (AI) is expected to help educators in the creative learning process by implementing long-life education and showing behavioral changes in a better direction cognitively, affectively, and psychometrically, especially in the era of society 5.0.
... These changes necessitate alterations to the content and direction of education to prepare people for the future by developing problem-solving abilities. The leading industrial countries, such as the United States, China, and South Korea, are preparing for a future with the education of an AI-based society [1]- [3]. Because education aims to enhance student skills required by society to prepare them for the future, the philosophy of supporting education and fundamental thinking skills is essential. ...
This study aims to examine the definition and attributes of artificial intelligence (AI) thinking to support AI education, so educators can determine how such education should be conducted in grades K–12. The text mining method was conducted using text crawling and co-word analysis to design and define AI thinking using the Python programming language. The cosine similarity and word2vec techniques were used to perform co-word analysis. Cosine similarity extracts paired words by assigning a weight according to the frequency of appearance. The skip-gram of word2Vec examines the surrounding words and predicts the paired words. According to the co-word analysis results, AI thinking is using an integrated thinking process to solve decision problems by discussing, providing, demonstrating, and proving processes. Moreover, AI thinking must be considered in future research on AI education. This study aims to serve as the foundational research to move forward in AI education.
... The National Science Foundation (NSF) of the United States launched AI4K12 Initiative to support setting up national guidelines for K-12 AI education [4]. AI4K12 is jointly sponsored by NSF, Association for the Advancement of Artificial Intelligence (AAAI), and Computer Science Teachers Association (CSTA). ...
As the need for teaching Artificial Intelligence (AI) for K-12 is increasing, discussions on what competencies teacher should have for effective teaching of AI is overlooked. In this work, we determine what teacher competencies are necessary for improving the teaching and learning of AI for K-12 with Technological Pedagogical Content Knowledge (TPACK) framework. First, we identify current AI education resources and investigate the core foundations of AI taught to K-12. Based on the findings, we propose teacher competency for K-12 AI education by analyzing AI curricula and resources using the TPACK framework. We conclude that teachers who teach AI to K-12 students require TPACK to construct, prepare an environment, and facilitate project-based classes that solve problems using AI technologies.
... An initiative based in the USA (ai4k12.org), sponsored by the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA), to develop a framework for artificial intelligence for K-12, has identified big ideas of artificial intelligence which they claim cover the richness of the field while being small enough to be manageable by teachers (Touretzky et al. 2019b) as part of computer or data science education (Magenheim and Schulte 2020). As with the Chinese curriculum, this approach also emphasises strongly the need for students to experience artificial intelligence, not only through interacting with artificial intelligence, but also through adapting and creating artificial intelligence systems. ...
Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.
هدّف البحث الحالي إلى التعرّف على مقتضيات تطبيق الذكاء الإصطناعي لدى طلبة الدراسات العليا، ولتحقيق هدف البحث تم إعتماد المنهج الوصفي، وبناء أداة البحث المتكونة من مقياس مقتضيات تطبيق الذكاء الإصطناعي لدى طلبة الدراسات العليا، وإعتمدت الباحثة على نظرية ثيرستون (Thirston,2022) في بناء مقياس مقتضيات تطبيق الذكاء الإصطناعي لدى طلبة الدراسات العليا، وتضمن المقياس (49) فقرة توزعت على سبع مجالات، وهي كالآتي: (المعرفي، والأكاديمي، والمعلوماتي، والبحثي، والتكنولوجي، والإجتماعي، والمهاراتي)، بواقع (7) فقرات لكل مجال، وتكونت عينة البحث من (400) طاباً وطالبة من طلبة الدراسات العليا في الجامعة المستنصرية، وتم إستخراج الخصائص السيكومترية لمقياس مقتضيات تطبيق الذكاء الإصطناعي لدى طلبة الدراسات العليا، والمتمثلة بالصدق والذي تم إستخراجه بطريقتين، وهما الصدق الظاهري والصدق العاملي، والثبات الذي تم إستخراجه بطريقتين هما: طريقة إعادة الإختبار وبلغ (0.75)، وبطريقة معادلة الفاكرونباخ وبلغ (0.79)، وتم التوصل إلى ضرورة وأهمية تطبيق الذكاء الإصطناعي لطلبة الدراسات العليا، وفي ضوء نتائج البحث خلُصت الباحثة إلى مجموعة من المقترحات والتوصيات.
الكلمات المفتاحية: مقتضيات تطبيق الذكاء الإصطناعي، طلبة الدراسات العليا.
Artificial Intelligence (AI) is driving ecological shifts and systemic reforms in education. As practitioners of educational reform, teachers’ behavioral intention to experience and accept the effectiveness of AI technologies will affect the quality of educational change. From an educational ecology perspective, this study explores the impact of core elements within three dimensions—technologies, pedagogies, and cultures—on teachers’ behavioral intention to use AI in an AI-enabled educational ecosystem (AI-e3) environment. The study uses a multi-level mediation model to analyze data of 4349 teachers from 189 primary and secondary schools from a western province of China. The results indicated that school-level dimensional elements, directly or indirectly, influenced teachers’ behavioral intention to use AI, mediated by teacher-level dimensional elements. The findings are relevant to school administrators and policy makers, who should consider the key influences on teachers’ behavioral intentions to use AI and promote the effective application of AI science for educational change.
Objectives . This paper explores teacher readiness for introducing artificial intelligence (AI) into Sri Lankan schools, drawing on self-efficacy theory. Similar to some other countries, Sri Lanka plans to integrate AI into the school curriculum soon. However, a key question remains: are teachers prepared to teach this advanced technical subject to schoolchildren? Assessing teacher readiness is crucial, as it is intricately linked to the overall success of this initiative and will inform the development of appropriate policies.
Participants . This study surveyed over 1,300 Sri Lankan public school teachers who teach Information and Communication Technology (ICT) using the snowball sampling approach. The respondents represent approximately 20% of the total ICT teacher population in Sri Lanka. Their readiness to teach AI was assessed using a general teacher self-efficacy scale specifically developed based on the well-established Self-Efficacy Theory. While key demographic factors like gender, education level, and educational background were also collected, their impact analysis is not included in this paper.
Study Method . The study was conducted based on the premise that teachers' readiness to teach AI hinges on their self-efficacy towards teaching AI in the classroom. This premise was substantiated through a review of existing research, and a conceptual model of teachers’ self-efficacy for AI instruction was developed. To assess this model, a nationwide survey targeting school ICT teachers was conducted. The questionnaire used in the survey was based on existing research on evaluating teacher self-efficacy. Data analysis, focusing on testing and validating the conceptual model, primarily employed the PLS-SEM approach.
Findings . This study identified several key findings: 1) Teachers generally reported low self-efficacy regarding their ability to teach AI, 2) Teachers' self-efficacy was most influenced by their emotional and physiological states, as well as their imaginary experiences related to teaching AI, 3) Surprisingly, mastery experiences had a lesser impact on their self-efficacy for teaching AI, and 4) Neither vicarious experiences (observing others teach AI) nor verbal persuasion had a significant impact on teachers' self-efficacy. Additionally, the study revealed that the teachers' own level of expertise in AI, along with their social capital, is insufficient to deliver a standard AI curriculum.
Conclusions . The analysis of the results found that Sri Lankan teachers currently lack the readiness to teach AI in schools effectively. Potential lapses in certain sources of self-efficacy were also identified. It further revealed the need for a more systemic approach to teacher professional development. Therefore, the study recommends further research exploring the potential of incorporating a socio-technical systems perspective into the government’s teacher training initiatives.
The purpose of this study is to introduce artificial intelligence (AI) robots to day-care centers to examine how children interact with robots in a natural environment and whether these interaction patterns change over time. Accordingly, the AI robot ALPHA MINI was introduced into two classes (33 children and 3 teachers) at daycare centers for 5 years olds to observe interactions among children, teachers, and a robot for three months. In addition, interviews with teachers were conducted after using the robot. The findings of the study revealed that children mainly use simple language with robots to engage in entertainment-oriented interactions, information acquisition interactions, and emotional interactions. Furthermore, while the interaction between children and robots remained relatively stable over time, it was noted that children leading the interaction with robots circulated, and the interaction patterns between peers mediated by robots has changed. Based on these findings, we highlight the need for prior deliberations on technical support, teacher education, and AI ethics as prerequisites for the integration of AI robots into early childhood classrooms.
There is an emerging interest in Artificial Intelligence (AI) teaching and learning in the K-12 setting. While some work has explored the educational content and resources used for this purpose, there is limited empirical evidence on the effectiveness of such AI education interventions. The primary objective of the systematic literature review presented in this paper was to examine research with empirical evidence reporting learning outcomes for teaching and learning AI in K-12 between 2019 and 2022. Through a rigorous selection process, a total of 28 studies were included in the final analysis out of 8,175 papers identified from five research databases using specific search terms. A content analysis method was used to synthesise the data. This paper outlines the focus on learners' context, the extent of empirical support for the pedagogical approaches, and the theoretical coverage of AI topics included in the studies. The majority of studies reported an improvement in both cognitive and affective learning outcomes. The paper concludes by highlighting key areas where additional research is needed in the future as well as the challenges associated with them. Although the findings are based on limited empirical studies, they suggest that a more learner-centred approach, context-aware pedagogical practices, and consistent constructs to measure AI learning outcomes could benefit teaching and learning AI in K-12 schools. Further research is needed to build on these insights.
Objective: This study investigated the differences in preschool children's perceptions of artificial intelligence (AI) and their distribution by latent profiles according to their experience with AI robots in daycare centers. Methods: The participants included 119 five-year-old children, 52 of whom had experience with AI robots in daycare centers and 67 of whom did not. Children's perceptions of AI were measured using the Godspeed scale from Bartneck et al.(2009). Data were analyzed using a t-test, latent profile analysis, and chi-square test. Results: The results showed that compared to the inexperienced group, the experienced group reported lower levels of animacy and perceived intelligence of AI robots, indicating higher levels of AI knowledge and understanding. In addition, the experienced group had a higher probability of belonging to the 'machine recognition' type than 'organism recognition' type, although the difference was not statistically significant. Conclusion/Implications: The findings suggest that experience with AI robots in daycare centers can improve children's AI knowledge and understanding. To further enhance this effect, it is necessary to increase the number of robots put into classrooms, and to consider various teaching media that reflect children's preferences. ❙key words Artificial intelligence education, Artificial intelligence robots, Social robots, AI literacy, Latent profile analysis I. 서 론 최근 인공지능(Artificial Intelligence; AI) 기술이 비약적으로 발전함에 따라 아동의 일상생활에 서도 인공지능 기술이 다양하게 활용되고 있다. 아동은 인공지능 기술을 탑재한 음성 스피커와 대화하며, 아동 개인의 학습 상황을 분석하여 맞춤형 문제를 제공하는 인공지능 기반 교육 서비 스를 경험한다. 또한, 미술관의 안내 로봇이 설명해주는 작품 해설을 들으며, 어린이집에서 아이 의 돌봄과 학습을 지원하기 위해 개발된 소셜 로봇과 놀이하며 상호작용하기도 한다. 이처럼 아 동의 일상생활에 인공지능 기술은 널리 퍼져 있으며, 아동은 인공지능 기술과 함께 놀고 배우며 성장하고 있다(Druga et al., 2017).
Computer science education has been researched in Israel for a few decades, both at the K-12 and the undergraduate levels. The rich variety of the investigated topics addressed from the very beginning issues beyond the introductory course and programming, including the nature of the discipline and its fundamental ideas and concepts, which are stable, unlike the more technological aspects. Understanding the nature of the discipline and mapping its fundamental ideas and concepts constitute the basis on which curricula stand. Therefore, we chose to organize this chapter around ideas and concepts of CS. In line with this perspective, we will discuss research of all age levels: K-12, undergraduate, and even the graduate level, as well as research relating to teachers. We will present design-based research, which accompanied the design of new curricula, as well as studies aiming at identifying phenomena, or investigating educational hypotheses. We will also point out current challenges and possible future directions.
Recently, Artificial Intelligence (AI), seen as an engineering domain, has been introduced into school education, but its pedagogy remains unclear. In general, group learning has been applied as a primary form of instruction in hands-on engineering activities. This learning approach is more common in higher education. School students are less mature; therefore, the benefits of adopting group learning as a pedagogical approach remain unclear. Group learning quality can be reflected by student–student interactions and dialogue within a group, and is classified into four types: collective, cooperating-in-parallel, dominant/defensive, and expert/novice. Accordingly, this experimental study involved 37 middle school students, and explored how they interacted within groups when learning AI through hands-on activities in the four group learning types. The Teacher Scheme for Educational Dialogue Analysis (T-SEDA) was used to code student–student interactions and compute their frequencies, and Lag Sequential Analysis was used to analyze the behavioral interaction sequence characteristics of the four group interaction patterns. The results showed that the expert/novice group learning had higher frequency of interaction, and also produced the longest, richest, and most complex sequences. The results suggest that this is the optimal approach to learning for younger students in hands-on AI activities as it encourages group members to interact with each other and reach a consensus. The results contribute to the literature by suggesting effective practices and confirming the use of T-SEDA in a new engineering school subject.
Part II of this book gave us an overview of AI literacy across educational levels. Several models of AI literacy education, in particular Bloom’s taxonomy, TPACK model, as well as the P21’s Framework for the 21st Century Learning (2009) that comprises key digital competencies that inform instructional designers what knowledge, skills, and attitudes students should equip with.
Based on the literature review in Chap. 5, we learn that even children as young as 4 years old have already grown up with AI. In our rapidly transforming digital world, equipping young learners with AI knowledge and skills will help ensure their employability and learning potential in their future. Moreover, AI is already present in their everyday life such as video games, AI toys, virtual assistants, and smart devices (e.g., Google Assistant, robotics dogs, Alexa devices). Teaching AI was not possible in the past; however, with age-appropriate curriculum and tools, primary students can now know and understand the working principles behind AI, use AI for learning purposes, and apply their knowledge to create artifacts to solve authentic problems. As such, there is a need to investigate the pedagogy, learning content, tools, and assessment methods involved to develop young learners’ AI literacy.
This chapter introduced various models of AI literacy education, in particular building on the twenty-first century skills framework to comprise important digital skill sets in today’s digital world. Such skills are essential given the challenges brought about by technological advances and changes in the global economic structure to keep the educational standards across regions. This chapter examines how AI is adapted from other digital literacies, the global movement of AI literacy education, and a broad scope of AI literacy from K–16 education.
Part I of this book gave us basic ideas about what AI literacy is, why it is important for all K–16 learners, as well as the theoretical frameworks and important theories involved in AI literacy education. However, students at each educational level have varied needs and intended learning outcomes. Therefore, the way of how AI literacy education is being implemented across levels of education should not be the same. Identifying specific age-appropriate approaches on different educational levels could inform schools, policymakers, educators, and parents on the design and development of adequate environment, pedagogy, learning content, technology, and assessment tools that best meet the needs of their students.
AI literacy is in high demand across industries. Thus, being literate in or learning AI should no longer be viewed as a specialized field under engineering but an ability that penetrates all disciplines (Johri, 2020). An analogy to extend this argument is by viewing traditional literacy. We would expect not only linguistics students to be competent in literacy, which is the proficiency to read and write, but also an appropriate level of literacy across any majors. Similarly, students at all levels and disciplines should develop AI literacy to stay competent in today’s world.
Artificial intelligence (AI) has influenced various industries (e.g., business, science, art, education) rather than merely computer science fields to improve working and learning efficiency. There are many AI-driven applications in daily life (e.g., smart home appliances, smartphones, Google, Siri) to enhance user experience and help people lead a better life. To prepare students to be more ready for tomorrow’s workplace, recent studies suggest the importance of learning AI for all students such as how computers learn from its data, the strengths and weaknesses of AI-driven applications, and its ethical concerns about AI (e.g., Long & Magerko, 2020; Ng & Chu, 2021; Sing et al., 2022). Studies suggest that even learners as young as kindergarteners should start to learn AI so that educators can bring them up to become responsible digital citizens (Su & Yang, 2022; Su & Zhong, 2022). This chapter posits AI literacy should be acquired by all learners, while differences lie in the content and approach at different education levels, i.e., K–16. For example, many countries have started to discuss how to incorporate AI curricula in primary and secondary levels. However, seldom do studies discover why AI should be taught at kindergarten and noncomputer university education. A list of reasons why people should learn AI is discussed in this chapter.
After understanding AI literacy from the perspective of human-design factor, this chapter presents a conceptual framework introducing an expanded view of AI literacy from educators’ perspectives. It moves beyond technological competencies and tries to identify a more holistic and broader understanding about AI literacy education. When using these novel AI tools to teach, educators need to be equipped with adequate technological literacy skills and knowledge. In this way, they can teach AI literacy and promote other digital competencies such as collaboration and communication among their students in AI-driven environments. Since teachers may not have rich technical knowledge to apply AI educational applications to facilitate their teaching. As one of the most important twenty-first-century competencies, AI literacy can be conceptualized as the knowledge, skills, and attitudes necessary to be competitive in the twenty-first-century workforce. Teacher education and professional development should be reworked to incorporate training in teaching key digital competencies.
Our twenty-first century is characterized by its rapid technological advancement. Our lifestyle and ways of interacting with people have changed significantly compared to around a decade ago in the early 2010s as AI technologies turn ubiquitous across industries and in our everyday lives. Artificial intelligence has spread across industries to enhance our living, learning, and working experience with exciting technological innovations such as computer vision, natural language processing, robotics and motion, machine and deep learning, and neural networks (Chen et al., 2022; Dong et al., 2021; Zawacki-Richter et al., 2019). Applications of AI have become in many parts of our everyday life (e.g., smart home appliances, smartphones, chatbots, search engines). In the field of education, schools began to use AI-enabled technologies to leverage students’ personalized learning and reduce teachers’ administrative work, thus offering more learning support and interactive learner experience (Roll & Wylie, 2016). Therefore, a field has gradually taken shape over the last few decades – AI in education (AIED).
This study aimed to determine how children in the age group of 6-10 perceive artificial intelligence, which enters their lives more and more every day, especially in the field of education. The research used phenomenology design within the scope of the qualitative research model. The study group consisted of 146 children aged 6-10 and their parents. Two forms, one for parents and one for children, were used to collect data in the study. The children were asked to complete the statement, "Artificial intelligence is like …………. Because ………….." The MAXQDA program was used to analyze the data, and the content analysis method was used to analyze and interpret the data obtained. It was determined that the children in the study group produced 12 metaphors to express the concept of artificial intelligence. Twelve metaphors are grouped into two main themes, animate and inanimate. When the reason for the phenomenon to which artificial intelligence is likened is examined, the answers given; It has been grouped under three main themes humanity-oriented, intelligence-oriented and robotic. As a result of the study, the metaphors produced provided the opportunity to understand, reveal and explain the perceptions of 6-10-year-old children regarding the concept of artificial intelligence.
La presente revisión tiene como objetivo describir el estado de la cuestión de la enseñanza-aprendizaje de la Inteligencia Artificial (IA) en primaria y secundaria, desde dos enfoques: (1) literatura proveniente de bases de datos científicas de alto impacto y (2) literatura gris. Para aquello, se utilizó la metodología de Barbara Kitchenham, como también, la adaptación de esta propuesta por Pablo Torres-Carrión. Así, para el primer enfoque, el proceso de búsqueda consistió en revisar cuatro bases de datos científicas multidisciplinarias (ACM Digital Library, IEEE Xplore, Scopus y Web of Science), considerando artículos de revistas, capítulos de libros y textos de congresos, publicados, en inglés, entre los años 2005 al 2021. Así, fueron seleccionados 67 artículos según los criterios de inclusión, exclusión, duplicados, y evaluación de la calidad. En el segundo enfoque, se realizó la búsqueda de la literatura gris mediante el buscador académico Google Scholar, con los mismos criterios de calidad antes mencionados; aquí se obtuvieron 33 documentos. Los hallazgos demuestran que las iniciativas de la enseñanza-aprendizaje de la IA es un campo con mucho crecimiento y con tendencia al alza a partir del 2018. Además, el machine learning y la robótica educativa, son las dos ramas vinculadas a la IA que actualmente se usan en la enseñanza de la IA en primaria y secundaria.
Artificial Intelligence (AI) is successfully intervening in areas such as education, health and industry. In education, AI is being used to improve educational processes. UNESCO considers as a new vision to involve AI not only as a didactic medium, but also as a science in which children can develop their intellect, through workshops, courses and curricula focused on the fundamentals of AI, allowing them to develop skills such as computational thinking and critical thinking. The present Systematic Literature Review was developed according to the methodological guidelines proposed by Barbara Kitchenham, with the objective of identifying how AI teaching-learning processes are carried out from early ages. Sixty-four studies were obtained between 2016-2021, where eleven countries are identified with AI teaching initiatives, through workshops, courses and projects to update their curricula; machine learning and educational robotics are the two branches linked to AI that are currently taught to children and adolescents.
Fog computing model was designed to provide computing at network’s edge among Internet of Things (IoT) devices. Its main purpose was to overcome the problems faced in cloud computing and provide services more effectively and efficiently. Being an extension of cloud computing, many security and privacy challenges that were faced by cloud computing are inherited by fog computing also. This paper presents different security and privacy challenges and work done by different researchers to overcome those challenges. Different techniques are represented with various metrices which they try to solve. We also proposed a method using ciphertext and shared key method to solve the problem of authentication and provide secure data sharing among the nodes. This method is supposed to be better than others in term of providing confidentiality, access control, authenticity and also protect from multiple splitting of private key.KeywordsFog computingAuthenticitySecurityAttribute-based encryption
The rapid increase of artificial intelligence and machine learning tools and technologies around us has led to a rise in the daily interaction between humans and these technologies. Children nowadays are very likely to interact with these tools in different contexts, such as at home, recreation centres, and schools. While some children are already exposed to and working with these technologies, others are still far behind in the digital world. In this paper, we use the 2020 Stack Overflow Developer Surveys dataset to examine the demography of K12 students who are already using machine learning tools at school or their workplace. Over 55% of the respondents are younger than 24 years. The finding shows that there is still a significant gender gap in the IT field, with only 2% of 138 respondents identified as female. Also, with only four African countries represented in the dataset, Africa is still behind regarding machine learning in K12.KeywordsK12 educationArtificial intelligenceStack overflow survey
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