The rapid increase in technological advancements in quantum information science and technology (QIST) necessitates a diverse workforce. The need for understanding how students, especially young women, develop QIST identity is an important consideration in career aspiration formation. With no existing framework for QIST identity development, this qualitative longitudinal exploratory case study combined aspects of identity frameworks in the QIST-adjacent fields of physics, computer science, engineering, and mathematics into a new framework to identify factors influencing QIST identity

The present study examined student outcomes from a quantum information science and technology (QIST) summer outreach program for U.S. secondary students. The program focused on foundational principles and skills from classical physics, quantum physics, and quantum computing. Students’ attitudes towards QIST learning and careers were measured through a pretest/posttest research design. Exploratory factor analysis was utilized to identify latent attitudinal themes, followed by comparisons of means to measure changes in these factors and analysis of covariance to assess whether these changes were

With the current growth in quantum information science and technology (QIST) industrial innovation, there is an increasing need to prepare precollege students for post-secondary QIST study and careers. This study examined high school student outcomes from a QIST outreach program held at both a research university and an informal science institution in the United States. The pre-/post-research design incorporated a newly developed QIST Integration Framework that included elements of science, technology, engineering, and mathematics (STEM) integration frameworks as well as conceptual change

Data science and artificial intelligence (AI) are poised to upend the way healthcare is delivered. But what happens when AI is trained on medical data that doesn’t reflect the population? A new semester-long course teaches high school students how embedded bias in machine learning affects healthcare discussions and decisions. The goals of the Data Science, AI, and You in Healthcare project include fostering community connections between educators, researchers, clinicians, and local stakeholders to prepare underrepresented students for STEM jobs—ultimately in fields where their background and

After participating in an afterschool program where they used the Common Online Data Analysis Platform (CODAP) to study time-series data about infectious diseases, four middle school students were interviewed to determine how they understood features of and trends within these graphs. Our focus was on how students compared graphs. Students were readily able to compare cumulative/total infection rates among two countries with differently sized populations. It was more challenging for them to link a graph of yearly cases to the corresponding graph of cumulative cases. Students offered reasonable

With the current growth in quantum information science and technology (QIST), there is an increasing need to prepare precollege students for postsecondary QIST study and careers. This mixed methods, explanatory sequential research focused on students’ affective outcomes from a one-week, 25-h summer program for U.S. high school students in grades 10–12.

The recent proliferation of artificial intelligence and machine learning (AI/ML) systems highlights the need for all people to develop effective competencies to interact with and examine AI/ML systems. We study shifts in five experienced high school CS teachers’ understanding of AI/ML systems after one year of participatory design, where they co-developed lessons on AI auditing, a systematic method to query AI/ML systems. Drawing on individual and group interviews, we found that teachers’ perspectives became more situated, grounding their understanding in everyday contexts; more critical

Research on children and youth’s participation in different roles in the design of technologies is one of the core contributions in child-computer interaction studies. Building on this work, we situate youth as advisors to a group of high school computer science teacher- and researcher-designers creating learning activities in the context of emerging technologies. Specifically, we explore algorithm auditing as a potential entry point for youth and adults to critically evaluate generative AI algorithmic systems, with the goal of designing classroom lessons. Through a two-hour session where

The Luquillo Long-Term Ecological Research (LTER) program in Puerto Rico has implemented a Data Jam initiative that fosters student engagement in ecological data science. In December 2023, over 175 middle and high school students presented 51 data-driven projects at a symposium in El Yunque National Rainforest. The initiative, rooted in the LTER’s extensive datasets, allows students to develop research questions, analyze data using the CODAP tool, and present findings. The program includes professional development for teachers, with workshops and Virtual Lab Meetings that equip them to guide

Solving many of the pressing issues facing the world today will require a deep and integrated understanding of science, technology, engineering, and mathematics (STEM). To prepare today’s K-12 students to tackle these challenges, STEM education must create opportunities to learn disciplinary content while inventing actionable solutions to messy, interdisciplinary problems. Learning frameworks, such as Project-Based Learning (PBL), Design-Based Learning (DBL), and Entrepreneurial-Based Learning (EBL), could support this reconceptualization of STEM education. New approaches are needed that