No abstract

a) a) b) b)Fig. 1. (a) VotestratesML's interface for building ML models based on voter profile data; (b) Students engaged in building models with VotestratesML (Note: all necessary permissions were collected to feature photos of students).The increased use of Artificial Intelligence, and in particular Machine Learning (ML) raises the need for widespread AI literacy, in three particular areas related to ML; understanding how ML works, the process behind creating ML models, and the ability to reflect on its personal and societal implications. Existing ML learning tools focus primarily on the first two areas, and to a lesser degree the third. In order to address this, we designed VotestratesML; a tool allowing K-12 students to build models and make predictions based on real world voting data. Based on in-situ deployments of VotestratesML, we reflect on opportunities and challenges for engaging K-12 students in understanding and reflecting on ML. We find that the design of VotestratesML supports students' engagement in all three areas of ML, through grounding ML in a known subject area and allowing for collaboration and competition.CCS Concepts: • Human-centered computing → Interactive systems and tools.

No abstract

No abstract

This systematic mapping review sheds light on how emerging technologies have been introduced and taught in various K–12 learning settings, particularly with regard to artificial intelligence (AI), machine learning (ML), the internet of things (IoT), augmented reality (AR), and virtual reality (VR). These technologies are rapidly being integrated into children's everyday lives, but their functions and implications are rarely understood due to their complex and distributed nature. The review provides a rigorous overview of the state of the art based on 107 records published across the fields of human–computer interaction, learning sciences, computing education, and child–computer interaction between 2010 and 2020. The findings show the urgent need on a global scale for inter- and transdisciplinary research that can integrate these dispersed contributions into a more coherent field of research and practice. The article presents nine discussion points for developing a shared agenda to mature the field. Based on the HCI community's expertise in human-centred approaches to technology and aspects of learning, we argue that the community is ideally positioned to take a leading role in the realisation of this future research agenda.