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Contemporary science is a field that is becoming increasingly computational. Today’s scientists not only leverage computational tools to conduct their investigations, they often must contribute to the design of the computational tools for their specific research. From a science education perspective, for students to learn authentic science practices, students must learn to use the tools of the trade. This necessity in science education has shaped recent K–12 science standards including the Next Generation Science Standards, which explicitly mention the use of computational tools and simulations. These standards, in particular, have gone further and mandated thatcomputational thinkingbe taught and leveraged as a practice of science. While computational thinking is not a new term, its inclusion in K–12 science standards has led to confusion about what the term means in the context of science learning and to questions about how to differentiate computational thinking from other commonly taught cognitive skills in science like problem-solving, mathematical reasoning, and critical thinking. In this paper, we propose a definition ofcomputational thinking for science(CT-S) and a framework for its operationalization in K–12 science education. We situate our definition and framework in Activity Theory, from the learning sciences, in order to position computational thinking as an input to and outcome of science learning that is mediated by computational tools.

 

Growing awareness of both the demand for artificial intelligence (AI) expertise and 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 chapters 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.