ABSTRACT:Researchers and policy makers often use the metaphor of an ever-narrowing pipeline to describe the trajectory to a science, technology, engineering or mathematics (STEM) degree or career. This study interrogates the appropriateness of the STEM pipeline as the dominant frame for understanding and making policies related to STEM career trajectories. Our review of pertinent literature and independent analysis of data from the National Educational Longitudinal Study of 1988 finds that the trajectory implied by the pipeline metaphor fails to describe the experience for nearly half of those who go on to become scientists or engineers, masks meaningful differences in trajectories by subfield, and informs policies that do little to diversify or increase the size of the STEM workforce. We suggest a pathway metaphor to better illuminate the multiple trajectories toward STEM degrees and careers and present four composite trajectories as useful categorizations of the individual paths taken by STEM graduates and career entrants.

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.

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