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In this conceptual paper, we review existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, (3) integrate ethics and justice. In the discussion, we address the challenges and opportunities of current approaches and suggest future directions for the design of learning activities.

TikTok, a popular short video sharing application, emerged as the dominant social media platform for young people, with a pronounced influence on how young women and people of color interact online. The application has become a global space for youth to connect with each other, offering not only entertainment but also opportunities to engage with artificial intelligence/machine learning (AI/ML)-driven recommendations and create content using AI/M-powered tools, such as generative AI filters. This provides opportunities for youth to explore and question the inner workings of these systems, their implications, and even use them to advocate for causes they are passionate about. We present different perspectives on how youth may learn in personally meaningful ways when engaging with TikTok. We discuss how youth investigate how TikTok works (considering data and algorithms), take into account issues of ethics and algorithmic justice and use their understanding of the platform to advocate for change.

As artificial intelligence/machine learning (AI/ML) applications become more pervasive in youth lives, supporting them to interact, design, and evaluate applications is crucial. This paper positions youth as auditors of their peers' ML-powered applications to better understand algorithmic systems' opaque inner workings and external impacts. In a two-week workshop, 13 youth (ages 14-15) designed and audited ML-powered applications. We analyzed pre/post clinical interviews in which youth were presented with auditing tasks. The analyses show that after the workshop all youth identified algorithmic biases and inferred dataset and model design issues. Youth also discussed algorithmic justice issues and ML model improvements. Furthermore, youth reflected that auditing provided them new perspectives on model functionality and ideas to improve their own models. This work contributes (1) a conceptualization of algorithm auditing for youth; and (2) empirical evidence of the potential benefits of auditing. We discuss potential uses of algorithm auditing in learning and child-computer interaction research.

Despite recent calls for including artificial intelligence (AI) literacy in K-12 education, not enough attention has been paid to studying youths' everyday knowledge about machine learning (ML). Most research has examined how youths attribute intelligence to AI/ML systems. Other studies have centered on youths' theories and hypotheses about ML highlighting their misconceptions and how these may hinder learning. However, research on conceptual change shows that youths may not have coherent theories about scientific phenomena and instead have knowledge pieces that can be productive for formal learning. We investigate teens' everyday understanding of ML through a knowledge-in-pieces perspective. Our analyses reveal that youths showed some understanding that ML applications learn from training data and that applications recognize patterns in input data and depending on these provide different outputs. We discuss how these findings expand our knowledge base and implications for the design of tools and activities to introduce youths to ML., Comment: accepted for publication at Proceedings of the International Conference of the Learning Sciences 2024

In this paper, we build on the 1971 memo "Twenty Things to Do With a Computer" by Seymour Papert and Cynthia Solomon and propose twenty constructionist things to do with artificial intelligence and machine learning. Several proposals build on ideas developed in the original memo while others are new and address topics in science, mathematics, and the arts. In reviewing the big themes, we notice a renewed interest in children's engagement not just for technical proficiency but also to cultivate a deeper understanding of their own cognitive processes. Furthermore, the ideas stress the importance of designing personally relevant AI/ML applications, moving beyond isolated models and off-the-shelf datasets disconnected from their interests. We also acknowledge the social aspects of data production involved in making AI/ML applications. Finally, we highlight the critical dimensions necessary to address potential harmful algorithmic biases and consequences of AI/ML applications.

Background and Context: While debugging is recognized as an essential practice, for many students, encountering bugs can generate emotional responses such as fear and anxiety that can lead to disengagement and the avoidance of computer programming. Growth mindsets can support perseverance and learning in these situations, yet few studies have investigated how growth mindsets emerge in practice amongst K-12 computing students facing physical computing debugging challenges. Objective: We seek to understand what (if any) growth mindset practices high school students exhibited when creating and exchanging buggy physical computing projects for their peers to solve during a Debugging by Design activity as part of their introductory computing course. Method: We focused on moment-to-moment microgenetic analysis of student interactions in designing and solving bugs for others to examine the practices students exhibited that demonstrated the development of a growth mindset and the contexts in which these practices emerged. Findings: We identified five emergent growth mindset practices: choosing challenges that lead to more learning, persisting after setbacks, giving and valuing praise for effort, approaching learning as constant improvement, and developing comfort with failure. Students most often exhibited these practices in peer-to-peer interactions and while making buggy physical computing projects for their peers to solve. Implications: Our analysis contributes to a more holistic understanding of students' social, emotional, and motivational approaches to debugging physical computing projects through the characterization of growth mindset practices. The presented inventory of growth mindset practices may be helpful to further study growth mindset in action in other computing settings.

Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect for data quality. To this end, we outline a set of four data design practices (DDPs) for designing inclusive ML models and share how we designed a tablet-based application called Co-ML to foster learning of DDPs through a collaborative ML model building experience. With Co-ML, beginners can build image classifiers through a distributed experience where data is synchronized across multiple devices, enabling multiple users to iteratively refine ML datasets in discussion and coordination with their peers. We deployed Co-ML in a 2-week-long educational AIML Summer Camp, where youth ages 13-18 worked in groups to build custom ML-powered mobile applications. Our analysis reveals how multi-user model building with Co-ML, in the context of student-driven projects created during the summer camp, supported development of DDPs including incorporating data diversity, evaluating model performance, and inspecting for data quality. Additionally, we found that students' attempts to improve model performance often prioritized learnability over class balance. Through this work, we highlight how the combination of collaboration, model testing interfaces, and student-driven projects can empower learners to actively engage in exploring the role of data in ML systems.

Background and Context: Few instruments exist to measure students' CS engagement and learning especially in areas where coding happens with creative, project-based learning and in regard to students' self-beliefs about computing. Objective: We introduce the CS Interests and Beliefs Inventory (CSIBI), an instrument designed for novice secondary students learning by designing projects (particularly with physical computing). The inventory contains subscales on beliefs on problem solving competency, fascination in design, value of CS, creative expression, and beliefs about context-specific CS abilities alongside programming mindsets and outcomes. We explain the creation of the instrument and attend to the role of mindsets as mediators of self-beliefs and how CSIBI may be adapted to other K-12 project-based learning settings. Method: We administered the instrument to 303 novice CS secondary students who largely came from historically marginalized backgrounds (gender, ethnicity, and socioeconomic status). We assessed the nine-factor structure for the 32-item instrument using confirmatory factor analysis and tested the hypothesized model of mindsets as mediators with structural equation modeling. Findings: We confirmed the nine factor structure of CSIBI and found significant positive correlations across factors. The structural model results showed that problem solving competency beliefs and CS creative expression promoted programming growth mindset, which subsequently fostered students' programming self-concept. Implications: We validated an instrument to measure secondary students' self-beliefs in CS that fills several gaps in K-12 CS measurement tools by focusing on contexts of learning by designing. CSIBI can be easily adapted to other learning by designing computing education contexts.

Understanding how youth make sense of machine learning and how learning about machine learning can be supported in and out of school is more relevant than ever before as young people interact with machine learning powered applications everyday; while connecting with friends, listening to music, playing games, or attending school. In this symposium, we present different perspectives on understanding how learners make sense of machine learning in their everyday lives, how sensemaking of machine learning can be supported in and out of school through the construction of applications, and how youth critically evaluate machine learning powered systems. We discuss how sensemaking of machine learning applications involves the development and integration of conceptual, creative, and critical understandings that are increasingly important to prepare youth to participate in the world.

Debugging, finding and fixing bugs in code, is a heterogeneous process that shapes novice learners' self-beliefs and motivation in computing. Our Debugging by Design intervention (DbD) provocatively puts students in control over bugs by having them collaborate on designing creative buggy projects during an electronic textiles unit in an introductory computing course. We implemented DbD virtually in eight classrooms with two teachers in public schools with historically marginalized populations, using a quasi-experimental design. Data from this study included post-activity results from a validated survey instrument (N=144). For all students, project completion correlated with increased computer science creative expression and e-textiles coding self-efficacy. In the comparison classes, project completion correlated with reduced programming anxiety, problem-solving competency beliefs, and programming self-concept. In DbD classes, project completion is uniquely correlated with increased fascination with design and programming growth mindset. In the discussion, we consider the relative benefits of DbD versus other open-ended projects.

As several critical issues in computing such as algorithmic bias, discriminatory practices, and techno-solutionism have become more visible, numerous efforts are being proposed to integrate criticality in K-16 computing education. Yet, how exactly these efforts address criticality and translate it into classroom practice is not clear. In this conceptual paper, we first historicize how current efforts in critical computing education draw on previous work which has promoted learner empowerment through critical analysis and production. We then identify three emergent approaches: (1) inquiry, (2) design and (3) reimagination that build on and expand these critical traditions in computing education. Finally, we discuss how these approaches highlight issues to be addressed and provide directions for further computing education research.