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With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technologies. It has become increasingly important for them to understand how these technologies are trained, what their limitations are and how they work. To introduce children to AI and Machine Learning (ML) concepts, recent efforts introduce tools that integrate ML concepts with physical computing and robotics. However, some of these tools cannot be easily integrated into building projects and the high price of robotics kits can be a limiting factor to many schools. We address these limitations by offering a low-cost hardware and software toolkit that we call the Smart Motor to introduce supervised machine learning to elementary school students. Our Smart Motor uses the nearest neighbor algorithm and utilizes visualizations to highlight the underlying decision-making of the model. We conducted a one week long study using Smart Motors with 9- to 12- year old students and measured their learning through observation, questioning and examining what they built. We found that students were able to integrate the Smart Motors into their building projects but some students struggled with understanding how the underlying model functioned. In this paper we discuss these findings and insights for future directions for the Smart Motor. 

Literature on professional identity formation is broad and complex. Currently, researcher identity development is an important topic that is beginning to be studied in different educational areas, including industrial and system engineering. Documenting researcher identity development is critical for designing student-centered programs. This is particularly crucial in doctoral students, as it may contribute to appropriate professional development support delivered by graduate programs. To properly analyze identity development, investigators have used tools from user experience (UX) methods such as journey mapping, which are invaluable. Journey maps document and visualize the steps that the users (in this case, novice researchers) take to achieve a goal, including the process that developing professionals undergo to become experts in their respective fields. Meanwhile, investigators have also used Behavior Over Time (BOT) Graphs in Systems Theory research, which assist in analyzing individual and organizational behavior trends over time. BOT graphs are also effective tools for tracking complex social behaviors. This paper proposes the potential to bridge gaps between UX and Systems Theory research methods, through their integrated application to understand researcher identity formation in doctoral engineering students. The integrated application offers a nuanced perspective on the formation of professional identity. This study benefits researchers by offering insights into new potential methodological approaches for mapping complex situations and behaviors. The examples in this paper focus on doctoral researcher identity formation but are not limited to this area. Practitioners and researchers can apply the proposed approach in various contexts, within and outside engineering. 

The Improving Students’ Sociotechnical Literacy in Engineering project aims to integrate social justice topics with technical knowledge in a first-year engineering course. The approach involves redesigning an existing intro to computing course with justice-based activities, supported by an Equity Learning Assistant (ELA) program. This program trains upperclass students to facilitate in-class discussions on equity and social justice. The project targets improvements in students' critical sociotechnical literacy and engineering identity. Activities include analyzing ethically complex data sets and developing equity-focused projects, while encouraging students to integrate social, economic, and political dimensions into their engineering work. This initiative spans four years (one pilot year plus three NSF-funded iterations) and involves a multidisciplinary research team of engineers and education researchers. 

IntroductionThis team science case study explores one cross-disciplinary science institute's change process for redesigning a weekly research coordination meeting. The narrative arc follows four stages of the adaptive process in complex adaptive systems: disequilibrium, amplification, emergence, and new order. MethodsThis case study takes an interpretative, participatory approach, where the objective is to understand the phenomena within the social context and deepen understanding of how the process unfolds over time and in context. Multiple data sources were collected and analyzed. ResultsA new adaptive order for the weekly research coordination meeting was established. The mechanism for the success of the change initiative was best explained by complexity leadership theory. DiscussionImplications for team science practice include generating momentum for change, re-examining power dynamics, defining critical teaming professional roles, building multiple pathways towards team capacity development, and holding adaptive spaces. Promising areas for further exploration are also presented.