As AI continues to rapidly change our world, questions and concerns about the ethical design and deployment of AI systems mount, leading to growing calls for direct attention to the ethics of AI in the design of learning experiences for youth (Garrett et al., 2020; Grosz et al., 2019). While this has often resulted in ethics ''add-ons'' to otherwise technical coursework (Saltz et al., 2019), we present a different approach: we have designed an online learning experience for high school-aged youth that directly integrates AI technical learning with attention to the ethical issues particular AI systems raise. Specifically, we have adopted a socio-technical approach to lesson design where we ground learning experiences in resonant contexts for youth that foster understating about how human and technical decisions (e.g., about what data sets are selected, or how bias is mitigated) can contribute to the ethics of how AI systems operate. To support this design goal, we formed a youth advisory group to identify resonant contexts and better understand the issues and concerns youth have about AI. For this presentation, we will discuss our design and its grounding principles, how the youth advisory group helped shape our current approach, and discuss lessons learned from our iterative design-based research. We will also discuss our efforts at creating a pro-social space for the online learning experience, challenges we've encountered, and how we've tried to address them. This work has been supported by the NSF ITEST solicitation under grant number 2241576.

The increasing presence and importance of Artificial Intelligence (AI) in our society has led to calls for its inclusion at all levels of education. However, the field is only beginning to understand what how AI learning experiences may be designed to be effective and developmentally appropriate, especially for young children. One challenge children encounter is in conceptualizing the “intelligence” of AI while they are still developing a metacognitive model of their own human intelligence. To investigate potential ways to address this, we developed a strategy, metacognitive embodiment, through which children are supported to (a) elicit a mental model of their own intelligent performance on a task and (b) compare that elicited model to an AI designed to accomplish the same task. From this study we found evidence suggesting that engaging children in metacognitive tasks in coordination with AI learning experiences (where the AI performs an analogous task) better positioned them for sensemaking about the AI’s intelligence.

Growing awareness of both the demand for artificial intelligence (AI) expertise and of the societal impacts of AI systems has led to calls to integrate learning of ethics alongside learning of technical skills in AI courses and pathways. In this paper, we discuss our experiences developing and piloting the TechHive:AI curriculum for high school youth that integrates AI ethics and technical learning. The design of the curriculum was guided by the following pedagogical goals: (1) to respond to the capacity-building need for critical sociotechnical competencies in AI workforce pathways; and (2) to broaden participation in AI pathways through intentional instructional design to center equity in learning experiences. We provide an overview of the 30-hour learning sequence’s instructional design, and our “4D Framework,” which we use as a heuristic to help students conceptualize and inspect AI systems. We then provide a focused description of one of three 8-hour modules that make up the sequence. Finally, we present evidence of promise from an exploratory study of TechHive:AI with a small sample of students, and discuss insights from implementation, including from our use of established resources for AI learning within the learning sequence as well as those created by our team.

In this experience report, we describe the Investigating Air Quality curriculum unit that integrates computational data practices with science learning in middle school science classrooms. The unit is part of the Coding Science Internship instructional model, designed to broaden access to computer science (CS) learning through scalable integration in core science courses, and through confronting barriers to equitable participation in STEM. In this report, we describe the core features of the unit and share preliminary findings and insights from student experiences in 13 science classrooms. We discuss affordances and challenges for student learning of computational data practices in formal science classrooms, and conclude with emerging recommendations for instructional designers.

Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.

Computational tools, and the computational thinking (CT) involved in their use, are pervasive in science, supporting and often transforming scientific understanding. Yet, longstanding disparities in access to learning opportunities means that CT's growing role risks deepening persistent inequities in STEM [2]. To address this problem, our team developed and studied two 10-lesson instructional units for middle school science classrooms, each designed to challenge persistent barriers to equitable participation in STEM. The units aim to position coding as a tool for doing science, and ultimately, encourage a broader range of students, and females in particular, to identify as programmers. Students who participated (n=391) in a recent study of the units demonstrated statistically significant learning gains, as measured on an external assessment of CT. Learning gains were particularly pronounced for female students. Findings suggest that students can develop CT through instruction that foregrounds science, and in ways that lead to more equitable outcomes.