This paper presents an eXplainable AI (XAI)-based classroom game “Breakable Machine” for teaching critical, transformative AI literacy through adversarial play and interrogation of AI systems. Designed for learners aged 10–15, the game invites students to spoof an image classifier by manipulating their appearance or environment in order to trigger high-confidence misclassifications. Rather than focusing on building AI models, this activity centers on breaking them—exposing their brittleness, bias, and vulnerability through hands-on, embodied experimentation. The game includes an XAI view to help students visualize feature saliency, revealing how models attend to specific visual cues. A shared classroom leaderboard fosters collaborative inquiry and comparison of strategies, turning the classroom into a site for collective sensemaking. This approach repositions AI education by treating model failure and misclassification not as problems to be debugged, but as pedagogically rich opportunities to interrogate AI as a sociotechnical system. In doing so, the game supports students in developing data agency, ethical awareness, and a critical stance toward AI systems increasingly embedded in everyday life.

In the fast-growing field of K–12 AI education, there is an urgent need for accessible, hands-on tools that introduce AI concepts and workflows to novice learners. In recent years, a variety of AI education tools have been introduced, ranging from coding environments to physical kits and robots. To provide an alternative to existing AI education tools, this paper presents a low-cost robotics kit (

This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16. The tool was designed for interventions on the fundamental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation algorithms. An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal experience on the platform as well as the experience of others. This approach seeks to enhance learners' data agency, AI literacy, and sensitivity to AI ethics. The paper includes a case example from 12 two-hour test sessions involving 209 children, using learning analytics to demonstrate how they navigated their social media feeds and the browsing patterns that emerged.

The datafication of education has enabled new means of supporting students, new topics to learn, new approaches for enhancing pedagogy, and new administrative tools. But it also risks student privacy and their data being used for purposes other than learning. Recently European legislators and data protection authorities have taken a strong stand towards K12 classroom technology in order to protect children's privacy. This article presents the tension between the right to education and the right to participate in digitalized society on the one hand-and the right to privacy on the other hand-and how to navigate a way between them. The article suggests that in a world of tightening regulation, educational technology providers need to understand that the purpose of data collection must arise from educational needs, not “nice to have” needs, and must be limited to what is necessary for education.

This study investigates the rarely-explored scaffolding processes in teaching artificial intelligence (AI), more specifically machine learning (ML), to K-12 students using educational technology. Focusing on a ML workshop within a children’s science camp, we observed 7-12 year-olds interacting with image classifiers using their own drawings, guided by an experienced computing teacher. Our analysis highlights the importance of the teacher’s role in a technology-rich environment in using diagnostic questions to reveal and address students’ misconceptions, aligning with the concept of contingent support. By linking theoretical concepts to practical activities, the teacher helped shift the focus from surface features to deeper processes, promoting advanced reasoning. The paper discusses a distributed scaffolding system combining teacher guidance, technological affordances, and peer interaction, crucial for making complex concepts accessible to young learners. These insights are important for educators and technology developers in enhancing K-12 ML/AI education.

This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms—tracking, profiling, and content recommendation—for novice learners. The tool provides a familiar, interactive interface that resonates with learners’ experiences with popular social media platforms, while also offering the means to “peek under the hood” and exposing basic mechanisms of datafication. Learners gain first-hand experience of how even the slightest actions, such as pausing to view content, are captured and recorded in their digital footprint, and further distilled into a personal profile. The tool uses real-time visualizations and verbal explanations to create a sense of immediacy: each time the user acts, the resulting changes in their engagement history and their profile are displayed in a visually engaging and understandable manner. This paper discusses the potential of XAI and educational technology in transforming data and digital literacy education and in fostering the growth of children’s privacy and security mindsets.

This paper introduces an AI education tool designed for novice learners to create machine learning (classifier) based applications. Advancing from Google’s Teachable Machine 2 and developed using the design science research methodology, the tool is piloted in 36 K-12 classroom sessions with 213 children and allows learners to easily navigate the complete ML workflow—from data collection to app deployment—without any programming skills. To evaluate how well the tool met children’s expectations children were asked, as part of the design process, to articulate their goals and intentions for their apps; then, after using the tool, to describe how well they perceived their final app realized their intention. The tool’s main novelty is its ability to create a standalone app by defining one or more actions to be triggered by each classifier result, and deploy that app to other devices. A no-code approach and fully integrated development environment reduces the need for technical skills, making AI learning more inclusive. The tool represents a significant step in making AI education accessible for early learners, with future enhancements aimed at expanding its capabilities.

K-12 computing education research is a rapidly growing field of research, both driven by and driving the implementation of computing as a school and extra-curricular subject globally. Within discipline-based education research, it is a new and emerging field, drawing on fields such as mathematics and science education research for inspiration and theoretical bases. The urgency around investigating effective teaching and learning in computing in school alongside broadening participation has led to much of the field being focused on empirical research. Less attention has been paid to the underlying philosophical assumptions informing the discipline, which might include a critical examination of the rationale for K-12 computing education, its goals and perspectives, and associated inherent values and beliefs. The goals of this research project are to understand the implicit and hidden values, perspectives and goals underpinning computing education at school. This will be achieved through a critical examination of a wide body of literature leading to the development of a categorization and framework.

Generative AI is a recent breakthrough in AI. While it has become a hot topic in computing education research (CER), much of the recent research has focused on e.g. issues of plagiarism or academic integrity. One problem spot with Generative AI is its susceptibility to various kinds of algorithmic bias. In this study, we collected data from an introductory computing course, where students experimented with text-to-image generative models and reflected on their generated image sets, in terms of biases, related harms, and possible fixes. Data were collected in Fall 2023 (pilot data in Fall 2022). Data included reports from 163 students. The results show (1) a variety of bias types observed by students related to gender, ethnicity, age, as well as a variety of bias types not observed by students, (2) two major types of attributions for the source of bias: bias caused by biases in the society and bias caused by data or algorithms, and (3) a number of potential harms associated with the biases, as well as attributions of those harms in specific contexts and use cases.

In this poster, we present the results from the co-design school projects on machine learning. We address social and educational challenges in artificial intelligence including security, privacy and education. We employ the participatory co-design approach, which facilitates children's right to be heard, and positions them as active partners, advisers, and designers in research and development work on technology and socio-technological practices.

The question of how to teach classical, rule-based programming has been driving much of the computing education research since the 1950s. In the K--12 (school) context, a consensus has emerged over time on the paradigmatic elements of computing education, which implicitly assumes a von Neumann computer executing instruction sequences guided by imperative programs. Within this framework, many researchers have focused on how to facilitate learners to develop an accurate mental model of what the computer does when it executes a piece of code.

Programming plays a paramount role in many educational policies and initiatives. However, the current focus on coding skills poses a risk of giving pupils an over simplistic and impoverished idea of what programming means and involves. Their experiences would be much more significant if learning were aimed at understanding the richness of the nature of programs. In fact, programs are strange creatures that escape simple definitions. They are real, in that they affect our real lives; they are abstract, in that they process abstract entities; and they are concrete, in that they take up space in digital devices memory, and can be copied, transferred, corrupted. Thus, understanding the multifaceted nature of programs is crucial knowledge for all citizens of the digital era, and a fundamental component of such an understanding is getting a sense of how programs are created and work (i.e., the programming process). To the best of our knowledge, there is no Nature of Programs framework (e.g., a set of statements that describe what the nature of programs is), that teachers and policy makers can use to shape their practice and targets. The goal of the WG is developing such a framework, by collecting and organizing contributions from CER, CS experts, and educators.

IEEE/ASEE Frontiers in Education turned 50 at the 2020 virtual conference in Uppsala, Sweden. This paper presents an historical retrospective on the first 50 years of the conference from a scientometric perspective. That is to say, we explore the evolution of the conference in terms of prolific authors, communities of co-authorship, clusters of topics, and internationalization, as the conference transcended its largely provincial US roots to become a truly international forum through which to explore the frontiers of educational research and practice. The paper demonstrates the significance of FIE for a core of 30% repeat authors, many of whom have been members of the community and regular contributors for more than 20 years. It also demonstrates that internal citation rates are low, and that the co-authoring networks remain strongly dominated by clusters around highly prolific authors from a few well known US institutions. We conclude that FIE has truly come of age as an international venue for publishing high quality research and practice papers, while at the same time urging members of the community to be aware of prior work published at FIE, and to consider using it more actively as a foundation for future advances in the field.

This Research Full Paper addresses the definition and implementation of Computational Thinking (CT) in K-12 education. CT is the focus of ongoing debate about the future of computing in schools, and this poses a number of challenges. Early discussions on the topic were plagued by vague and all-encompassing definitions of the term, which raised awareness of a need for school learning focused on emerging computational systems and their impact on society and the lives of citizens, but failed to address what should actually be included in the revised curricula. As some of those issues have been addressed, a number of problems remain. This paper analyses some recent and future developments in the field of computing, and expands the discussion on the vexed question of the nature and limits of computational thinking. We ask whether the current mainstream literature and curriculum for CT is well positioned in terms of the future towards which computing is heading. We conclude by summarising the implications these questions have for the future development of K-12 curricula and teaching practices.

The momentum around computational thinking (CT) has kindled a rising wave of research initiatives and scholarly contributions seeking to capitalize on the opportunities that CT could bring. A number of literature reviews have showed a vibrant community of practitioners and a growing number of publications. However, the history and evolution of the emerging research topic, the milestone publications that have shaped its directions, and the timeline of the important developments may be better told through a quantitative, scientometric narrative. This article presents a bibliometric analysis of the drivers of the CT topic, as well as its main themes of research, international collaborations, influential authors, and seminal publications, and how authors and publications have influenced one another. The metadata of 1,874 documents were retrieved from the Scopus database using the keyword “computational thinking.” The results show that CT research has been US-centric from the start, and continues to be dominated by US researchers both in volume and impact. International collaboration is relatively low, but clusters of joint research are found between, for example, a number of Nordic countries, lusophone- and hispanophone countries, and central European countries. The results show that CT features the computing’s traditional tripartite disciplinary structure (design, modeling, and theory), a distinct emphasis on programming, and a strong pedagogical and educational backdrop including constructionism, self-efficacy, motivation, and teacher training.

This innovative practice full paper is grounded in the societal developments of computing in the 2000s, which have brought the concept of information literacy and its many variants into limelight. Widespread tracking, profiling, and behavior engineering have set the alarms off, and there are increasing calls for education that can prepare citizens to cope with the latest technological changes. We describe an active concept, data agency, that refers to people's volition and capacity for informed actions that make a difference in their digital world. Data agency extends the concept of data literacy by emphasizing people's ability to not only understand data, but also to actively control and manipulate information flows and to use them wisely and ethically.This article describes the theoretical underpinnings of the data agency concept. It discusses the epistemological and methodological changes driven by data-intensive analysis and machine learning. Epistemologically the many new modalities of automation are non-reductionist, non-deterministic, and statistical; the models they rely on are soft and brittle. This article also presents results from a pilot study on how to teach central machine learning concepts and workflows in K-12 through co-creation of machine learning-based solutions.

This Research to Innovative Practice Full Paper presents a multidisciplinary, design-based research study that aims to develop and study pedagogical models and tools for integrating machine-learning (ML) topics into education. Although children grow up with ML systems, few theoretical or empirical studies have focused on investigating ML and data-driven design in K-12 education to date. This paper presents the theoretical grounds for a design-oriented pedagogy and the results from exploring and implementing those theoretical ideas in practice through a case study conducted in Finland. We describe the overall process in which middle-schoolers (N = 34) co-designed and made ML applications for solving meaningful, everyday problems. The qualitative content analysis of the pre-and post-tests, student interviews, and the students' own ML design ideas indicated that co-designing real-life applications lowered the barriers for participating in some of the core practices of computer science. It also supported children in exploring abstract ML concepts and workflows in a highly personalized and embodied way. The article concludes with a discussion on pedagogical insights for supporting middle-schoolers in becoming innovators and software designers in the age of ML.

Artificial intelligence and machine learning are making their ways rapidly to K-12 education. Google Teachable Machine, powered by convolutional neural networks, provides an easy-to-use yet powerful tool for classification tasks. We conducted a series of co-design workshops with primary school children, where they explored and designed their own machine learning powered applications with Google Teachable Machine. Our results show that Google Teachable Machine is a feasible tool for K-12 education. The trained machine learning models are lightweight and computationally efficient, and the applications are usable even with low-end mobile devices. The students and teachers appreciated the multidisciplinary and inclusive workshop, which supports development of transversal competencies in accordance to the national primary school curriculum.

Computing education, in the sense we know it today, was born in the first half of the 1950s with the advent of mass-produced storedprogram computers [15]. Programming, using a vocabulary of a few dozen machine language commands in octal code, was not considered especially time-consuming to learn: In the first conference on training personnel for the computing machine field in 1954, the spokesperson for Remington Rand stated that "one manufacturer indicated that programmers may be trained in two weeks, while another requires twelve", depending on the computer and problem types [4]. After the birth of high-level languages, a 1960 conference of university computing center directors stated that programming is "now simple enough so that an undergraduate . . . can begin to use a particular machine after a few hours of instruction" [12].

This full paper in the research track presents how individuals in computing education may have role models that represent different ways of engaging in the discipline and/or profession as a student or a professional. The study is based on two rounds of interview-based data collection at a department of computing: a longitudinal study of undergraduate students' view of the discipline, and an examination of their teachers' experiences as role models in computing education. Our results challenge traditional views of role models as those who set the norms, presenting instead role models as potentially capable of change, at different scales (including none), depending on their level of power. These role models are students, academics, and other professionals. We show that the potential of role models must be understood with respect to how engagement in computing is constructed in social interaction. Actions are suggested for relevant stakeholders to reflect on which role models are receiving more or less exposure than they should and how through these role models participation in computing can be broadened in terms of not only diverse backgrounds but also ways of engaging in computing.

Research methodology education is one of the less studied and discussed areas of computing education. Underplaying methodological education starts from computing curricula, which discuss methodology to different degrees, depending on the branch of computing. In computer science education research, programming courses have been analyzed through and through, but methodology courses in computing are largely devoid of course descriptions, analytic studies, and experimental studies. This paper presents the learning objectives, contents, and arrangements for a fully online graduate level course on research methodology and research design in computing. The course was run twice in a relatively large school of computing (6915 students) and it included students from a neighboring institution of the same size. Students' (N=136) learning was analyzed from multiple viewpoints. Their final work was analyzed qualitatively by course facilitators as well as scored on a 90-point scale. Their coursework was qualitatively reviewed and graded by their peers and facilitators. The effect of students' learning approaches to course results was analyzed using Biggs' R-SPQ-2F questionnaire. Student feedback was collected using a slightly modified course feedback questionnaire of the university. This paper presents the course arrangements, course results, and analysis of students' learning.

Research methodology is a quintessential component of science, but methods differ greatly between sciences. In computing, methods are borrowed from many fields, which causes difficulties to methodology education in computing. In our methodology courses in computing, we have observed a number of core and threshold concepts that affect students' success. This essay describes a work in progress towards understanding those core and threshold concepts in methodology education in computing, classified along two dimensions. We classify methodological concepts in terms of standard elements of research design in students' projects in computing, and in terms of their centrality and difficulty. We present examples of three types of troublesome knowledge concerning methodology: the strangeness and complexity of methodological concepts, misimpressions from everyday experience, and reasonable but mistaken expectations.

One-to-one computing is an active and widely researched topic in educational technology. Its benefits include, for instance, easily up datable material base, anywhere-anytime learning, adaptability, and simulated experiments in science. The use of one-to-one computing in a developing country context has recently become an active research topic. However, the materialization of the educational benefits requires proper contextualization regarding the necessary pedagogical, organizational, institutional, and other types of adaptation. This paper presents preliminary results from an action research study in a primary school in rural Tanzania. In that study, the utilization of one-to-one computing in a combination with modern pedagogical approaches to teach ICT and health care topics was studied.

All major computing curricula recommendations mention methodological skills and knowledge as an important learning objective in undergraduate and graduate education. None of those curricula recommendations, however, include a methodology course for students. One reason for that lack might be the stunning diversity of computing fields and the unique methods each branch of computing uses in their research. A methodology course in computing has to make a choice between three options: a narrow but deep specialization in some techniques and methods, a broad but superficial covering of a large number of methods, and a higher-level view on the principles of methodology and research design. This paper adopts the high-level approach, and presents a course description for a methodology course that aims at providing students understanding of how the elements of a research study link together.

One of the most debated areas of computer science education is how to arrange programming courses. One of the debates is concerned with the amount of guidance a learning environment should grant to the learner. This research study reports on development and testing of a model where students work on their homework under guidance, facilitated by active student-teacher collaboration, continuous feedback, and student support. While qualitative results, observations, and student feedback about the intervention were exclusively positive, controlled experiment showed no significant advantage over the control group. This paper reports the results of the experiment described above, and suggests ten hypotheses for further research.

This paper focuses on understanding and developing learning environments for computer science education. We present two models that we have successfully used in European and African contexts. The first model, Computer Science Learning Environments (CSLE), presents seven dimensions of computer science courses, which should be considered in learning environment design for computer science. The second model, Investigative Learning Environment (ILE), presents an action plan model, inspired by action research, for combining educational research and computer science teaching. In the empirical section we outline two case studies where these models were used to design and implement computer science learning environments in two different learning contexts. In the first case in University of Helsinki, Finland, we developed and studied a method of learning-by-inventing in a robotics programming course. That course was designed around problem discovery and inventing, and it employed LEGO® Mindstorms robots. In the second case in Tumaini University, Tanzania, we designed an environment for studying and improving introductory programming courses. Both models showed to be useful for designing, implementing, developing, and analyzing the courses in both learning contexts.

Contextualization of curriculum and course contents has been central to development of IT education at Tumaini University in rural Tanzania. However, as the development of the IT program has progressed, pedagogical challenges have become increasingly evident. The pedagogical difficulties materialize most markedly in programming courses. This paper reports an empirical study of students' and teachers' perceptions of challenges of programming education in Tanzania. The results support the anecdotal evidence from various developing countries that programming education is hindered by shallow learning strategies, unfamiliar pedagogical approaches, language problems, extrinsic motivations, free riding in group assignments, and cultural differences.

Due to an urgent national need for more IT professionals, developing countries spawn academic IT programs at an increasing rate. Those programs are often replicated from similar programs in Europe and America. Students' preferences concerning their education are, however, rarely inquired. This research study explored and analyzed Tanzanian IT students' views of challenges in their studies, as well as their views of the most important factors for their learning. A longitudinal perspective was introduced, as changes in results between 2008 and 2011 were analyzed. The findings suggest that there is a need for increased practical training, development of facilities, improvement of course design, and increased feeling of ownership and empowerment.

This panel will consider the issues related to undergraduate research in computer science from a global perspective. Panelists from different countries and varied backgrounds will relate their experiences in conducting such research.