The sudden prevalence of Natural Language Processing (NLP) systems, such as ChatGPT, creates a need for resources to support teachers and students in navigating their use in the classroom. While HCI research has contributed hands-on activities supporting AI literacy, NLP is less explored, and there is a lack of research on how to integrate these tools into teachers’ practices. To address this, we conducted an 18-month Participatory Design process with high-school teachers comprising prototyping, workshops, classroom teaching, and dissemination activities. Through this process, we developed and contribute The Engine Room, a public website with novel digital and unplugged NLP activities that are modular and built around customisable datasets. Our findings show how participatory methods can be used to empower teachers in integrating AI literacy activities into their teaching across various subjects and with a sustained impact. We discuss how a participatory approach can address challenges of designing AI literacy tools and activities.

Expanded use context of an educational toolkitEducator Figure 1: Existing HCI research mainly focuses on demonstrating novel approaches and tools to engage students with machine learning through embodied interaction.With ML-Machine, we expand the context of use and investigate how an educational toolkit can support teachers and education professionals in developing material, designing activities, and deploying embodied activities with machine learning.(Image used with permission from the children and their legal guardians)

Figure 1: To support teachers and students in acting skillfully around AI, we explore how AI technologies and methods can interplay with knowledge and skills in existing school subjects.From left to right: Teachers engaging with AI activities as part of a workshop; students discussing sentences which they are annotating to train a language model; a teacher's lesson plan which integrates machine learning activities into mathematics.

CS Unplugged: computing education activities without digital tools have successfully engaged young students in fundamental computing concepts. While CS Unplugged fosters qualities such as collaboration and kinesthetic engagement, it requires significant facilitation and offers little room for students' experimentation. Addressing these limitations, we augment unplugged activities with tangible computing by designing and evaluating Bit:sort. Bit:sort supports young students' exploration of sorting algorithms using the micro:bit platform, maintaining the embodied elements of unplugged activities while using tangible computing to direct the activity and provide motivation for collaborative learning. We conducted a study with six groups of 4th-grade students (n=35). Our findings indicate that while students struggle to articulate their problem-solving strategies verbally, they demonstrate algorithmic reasoning through kinesthetic explorations. We discuss the qualities and limitations of augmenting CS Unplugged and propose design guidelines for replugged activities to foster scalable and exploratory learning experiences while maintaining core qualities of the unplugged approach.

ml-machine.org is a web- and micro:bit-based educational tool for building machine learning models designed to enable more widespread teaching of AI literacy in secondary education. It has been designed as a research product in collaboration with partners from the educational sector, including the Danish Broadcasting Corporation and the Micro:bit Educational Foundation. ml-machine.org currently has more than 5000 unique users and is used in schools and teacher training. It is publicly available and promoted on the broadcasting corporation’s platforms. We describe the two-year process of developing and disseminating ml-machine.org. Based on interviews with partners and educators, we report on how ml-machine.org supports inquiry into the adoption and appropriation of such educational tools. We also provide insights on working with formal education infrastructures in order to scale and integrate a research product into teacher practices. Based on these experiences, we propose infrastructure as a novel quality of research products.

Advances in artificial intelligence present a need for fostering AI literacy in workplaces. While there is a lack of research on how this can be achieved, there are documented successful approaches in child-computer interaction (CCI), albeit aimed at K-12 education. We present an in-vivo explorative case study of how CCI approaches can be adopted for adult professionals via a full-day workshop developed in collaboration with a trade union to upskill workers. Analyzing data from pre- and post-surveys, a follow-up survey, and materials produced by participants (n=53), we demonstrate how this increased participants’ knowledge of AI while their self-efficacy and empowerment did not improve. This is similar to findings from K-12 education, pointing to self-efficacy and empowerment as major challenges for AI literacy across sectors. We discuss the role of ambassadorships and professional organizations in addressing these issues, and indicate research directions for the CHI community.

Children grow up in a data economy but grasping how their data are part of this eco-system and how data become valuable to others can be difficult. This work explores how to design tools and activities which support children’s critical data literacy for K-9 education. We bring together two strands of work; First, insights from a co-design process where teachers and researchers designed tools and activities for teaching critical data literacy which they deployed in a lower secondary education classroom. Second, insights from didactic theories from maths and computer science education about working with multiple and rich representations of complex and intangible phenomena. Based on this we contribute three principles for enriching pupils’ relationship to data in order to inform future research into how pupils can be scaffolded in forming richer relationships to the data-driven technologies in their everyday lives in order to retain agency in a data-driven world.

Machine Learning (ML) is often used invisibly in everyday applications with little opportunity for consumers to investigate how it works. In this paper, we expand recent efforts to unfold what students should know about ML and how to design tools and activities allowing them to engage with ML. To do so, we explore how to make processes and aspects of ML tangible through the design of the Machine Learning Machine (MLM); a tangible user interface which enables students to create their own data-sets using pen and paper and to iteratively build and test ML models using this data. Based on insights from the design process and a preliminary pilot study with the MLM, we discuss how a tangible approach to engaging with ML can spur curiosity in students and how the iterative process of improving ML models can encourage students to reflect on the relation between data, model and predictions.

The increased use of machine learning (ML) in society raises questions of how ethical dilemmas inherent in computational artefacts can be made understandable and explorable for students. To investigate this, we developed a card-based design workshop that allows students to reflect on ethical dilemmas by designing their own ML applications. The workshop was developed in an iterative process engaging four high school classrooms with students aged 16-20. We found that scaffolding students in designing meaningful ML systems served to qualify their ethical reflections. Further students' design processes allowed them to engage with the ethical dilemmas and to tie these to the properties of the technology and to their design decisions. We suggest seeing technology-close discussions about ethics as a goal in design processes, and prototyping as a means to ground these discussions in students' own design decisions, and we contribute a workshop format and design artefacts that allow for this.

Kirigami Actuators are two-dimensional patterns that allow the translation of a simple actuation in one dimension into a complex transformation in another. Kirigami Actuators represent one metamaterial strategy that designers of Shape-changing Interfaces could utilize to minimize the size and complexity costs of actuation. Metamaterials yield great promise for HCI and Shape-changing Interfaces in particular. In an effort to reveal the promise of Kirigami Actuators for the design of Shape-changing Interfaces, this pictorial presents several design tactics for a specific Kirigami Actuator: The Lift pattern. These tactices outline how different components of the pattern can be changed to strategically alter the transformational qualities of the pattern. Initial concept sketches are presented as inspiration for future work.