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The hero
This title examines the role and theme of the hero archetype in Harry Potter and the Sorcerer's Stone, To Kill a Mockingbird, 12 Years a Slave, The Scarlet Letter, and Little Women. It features four analysis papers that consider the hero theme, each using different critical lenses, writing techniques, or aspects of the theme.
BOOK
Artificial Intelligence in K-12 Education: eliciting and reflecting on Swedish teachers' understanding of AI and its implications for teaching & learning
Uncovering patterns and trends in vast, ever-increasing quantities of data has been enabled by different machine learning methods and techniques used in Artificial Intelligence (AI) systems. Permeating many aspects of our lives and influencing our choices, development in this field continues to advance and increasingly impacts us as individuals and our society. The risks and unintended effects such as bias from input data or algorithm design have recently stirred discourse about how to inform and teach AI in K-12 education. As AI is a new topic not only for pupils in K-12 but also for teachers, new skill sets are required that enable critical engagement with AI. AI literacy is trying to close the gap between research and practical knowledge transfer of AI-related skills. Teachers' AI-related technological, pedagogical and content knowledge (TPACK) are important factors for AI literacy. However, as teachers' perspectives, beliefs and views impact both the interpretation and operationalisation of curriculum. this study explores teachers' and teacher educators' understanding and preconceptions of AI to inform teacher education and professional development. To gain a comprehensive understanding of teachers’ conceptualisations regarding AI an anonymous questionnaire together with focus group discussions were employed. The qualitative content analysis underpinned by the theoretical framework Intelligent TPACK reveals that teachers' AI-related content knowledge is generally gained through incidental learning and often results in pre- and misconceptions of AI. Our analysis also revealed several potential challenges for teachers in achieving core constructs of Intelligent TPACK, examples of such challenges are vague and unclear guidelines in both policy and curriculum, a lack of understanding of AI and its limitations, as well as emotional responses related to participants' preconceptions. These insights are important to consider in designing teacher education and professional development related to AI literacy.
Journal Article
K-12 Education in the Age of AI: A Call to Action for K-12 AI Literacy
The emergence of increasingly powerful AI technologies calls for the design and development of K-12 AI literacy curricula that can support students who will be entering a profoundly changed labor market. However, developing, implementing, and scaling AI literacy curricula poses significant challenges. It will be essential to develop a robust, evidence-based AI education research foundation that can inform AI literacy curriculum development. Unlike K-12 science and mathematics education, there is not currently a research foundation for K-12 AI education. In this article we provide a component-based definition of AI literacy, present the need for implementing AI literacy education across all grade bands, and argue for the creation of research programs across four areas of AI education: (1) K-12 AI Learning & Technology; (2) K-12 AI Education Integration into STEM, Language Arts, and Social Science Education; (3) K-12 AI Professional Development for Teachers and Administrators; and (4) K-12 AI Assessment.
Journal Article
Guest editorial
The development and prevalence of technology has not only facilitated favorable conditions for self-directed learning but also made it a core competence of today’s world. Thus, cultivating K-12 learners’ capability of utilizing technology to engage in self-directed learning becomes an essential task in education. In this editorial of the special issue, we join the authors to search for insights into what to and how to support K-12 learners’ capacity of self-directed learning with technology. Through reviewing the framework of self-directed learning and exploring the role of technology, we call for research that examine mechanisms and issues that related not only to fostering learners’ regulation of their learning process, but also to supporting learners in initiating and taking responsibilities for self-directed actions to the benefit of oneself and one’s community.
Journal Article
Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review
Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.
Journal Article
Machine Learning and the Five Big Ideas in AI
This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences.
Journal Article
Mapping the public understanding of computational thinking education: Insights from social Q＆A platform discussions
With the growing popularity of computational thinking (CT) classes in K-12 schools, it is important to investigate public perceptions of these initiatives. Analyzing public discussions and opinions provides valuable insights that can inform future educational policies and reforms. In this paper, we collected questions and answers related to CT education on the Quora platform. Next, we applied a topic modeling approach to find out major topics in online discussions. Through analysis, we identified 6 topics in questions and 14 topics in answers. Our findings revealed that people showed great interests but also uncertainty about CT education learning outcomes. Many people asked for suggestions on CT learning tools and platforms, but they struggled to identify appropriate information to support their learning needs. Among their answers, while people held positive attitudes toward CT education, they were concerned about the difficulties their children faced in the learning process and the problem of educational equity. Moreover, since CT practices cultivate information literacy skills for children in the 21st century, the benefits of CT education might be overestimated. These findings deepen our understanding of CT education, which could inform education policies and future research directions.
Journal Article
Artificial Intelligence in K-12 Education
Uncovering patterns and trends in vast, ever-increasing quantities of data has been enabled by different machine learning methods and techniques used in Artificial Intelligence (AI) systems. Permeating many aspects of our lives and influencing our choices, development in this field continues to advance and increasingly impacts us as individuals and our society. The risks and unintended effects such as bias from input data or algorithm design have recently stirred discourse about how to inform and teach AI in K-12 education. As AI is a new topic not only for pupils in K-12 but also for teachers, new skill sets are required that enable critical engagement with AI. AI literacy is trying to close the gap between research and practical knowledge transfer of AI-related skills. Teachers' AI-related technological, pedagogical and content knowledge (TPACK) are important factors for AI literacy. However, as teachers' perspectives, beliefs and views impact both the interpretation and operationalisation of curriculum. this study explores teachers' and teacher educators' understanding and preconceptions of AI to inform teacher education and professional development. To gain a comprehensive understanding of teachers’ conceptualisations regarding AI an anonymous questionnaire together with focus group discussions were employed. The qualitative content analysis underpinned by the theoretical framework Intelligent TPACK reveals that teachers' AI-related content knowledge is generally gained through incidental learning and often results in pre- and misconceptions of AI. Our analysis also revealed several potential challenges for teachers in achieving core constructs of Intelligent TPACK, examples of such challenges are vague and unclear guidelines in both policy and curriculum, a lack of understanding of AI and its limitations, as well as emotional responses related to participants' preconceptions. These insights are important to consider in designing teacher education and professional development related to AI literacy.
Journal Article
Lessons Learned for AI Education with Elementary Students and Teachers
With accelerating advances in artificial intelligence, it is clear that introducing K-12 students to AI is essential for preparation to interact with and potentially develop AI technologies. To succeed as the workers, creators, and innovators of the future, we argue students should encounter core concepts of AI as early as elementary school. However, building a curriculum that introduces AI content to K-12 students presents significant challenges, such as connecting to prior knowledge, developing curricula that are meaningful for students, and creating content that teachers feel confident to teach. To lay the groundwork for elementary AI education, we investigated the everyday experiences and ideas of students in grades 4 and 5 (ages 9 to 11) about AI to inform possible entry points for learning. This yielded themes around student conceptions, examples, and ethics of AI. For each theme, we juxtapose the student ideas with the teachers’ reflections on those ideas as frames of reference to consider in co-designing curricular approaches.
Journal Article
Commentary for the International Journal of Artificial Intelligence in Education Special Issue on K-12 AI Education
In this commentary, I review the articles in the IJAIED Special Issue on K-12 AI Education. The articles offer compelling motivation for early AI education and cover an impressive range of approaches, grade levels, and perspectives. Despite the differences, there is coherence across the articles in terms of the goals to address AI awareness, knowledge, skills, and ethics. Deep consideration has gone into creating developmentally appropriate AI content, which is arguably the greatest challenge for a complex topic like AI. However, as we find in many emerging topics in education, the demand for curricula and lessons has outpaced the capacity of the field to do sufficient empirical research on how kids learn about AI. Evidence for many of the design choices reflected in the proposals put forth in this special issue is still emerging. The authors have done an admirable job of organizing their ideas around principles from the learning sciences and connecting their efforts to more general curriculum design efforts, such as the K12 CS Framework (2016). The next step, which is promoted by all of the authors in the special issue, is to define a research agenda to provide an empirical basis for the design of early AI learning experiences and inform future iterations of the curricula and frameworks proposed.
Journal Article