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

Generative Artificial Intelligence (AI) introduces exciting new possibilities and challenges to the established field of computational modeling education. The ten posters in this symposium provide different perspectives on the changing landscape and present examples of educational programs, professional development strategies, pedagogical approaches, and digital tools that have helped learners and educators develop the skills needed to interrogate and cocreate scientific computational models with AI. The posters address each stage of computational modeling education, including reading and

This study evaluates the effectiveness of a machine learning (ML) integrated science curriculum implemented within the Science Research Mentorship Program (SRMP) for high school youth at the American Museum of Natural History (AMNH) over 2 years. The 4-week curriculum focused on ML knowledge gain, skill development, and self-efficacy, particularly for under-represented youth in STEM. Suggested citation: Rabinowitz, G., Moore, K.S., Ali, S. et al. Study of an effective machine learning-integrated science curriculum for high school youth in an informal learning setting. IJ STEM Ed 12, 23 (2025)

Educators are facing the challenge of redesigning curricula to integrate artificial intelligence (AI) and machine learning (ML) methods in a way that is relevant and engaging to youth. Design-based research (DBR) presents a unique opportunity to conduct iterative re-design of these curricula while incorporating feedback from stakeholders including youth and professionals in the field. In this paper we present a mixed methods analysis of the iterative design of an informal science-integrated ML curriculum for high school youth enrolled in a four-week summer program. Each step of the two year

Student engagement during learning serves as a critical predictor of academic success and plays a pivotal role in nurturing interest and readiness for future careers. As digital platforms become increasingly important to learning, it is essential that we understand how the interactions that students have with them reflects their engagement with learning. Previous research has often modeled engagement in a fully online context, where students pursue lessons independently and outside the influence of the classroom, paced and structured by digital systems. However, in STEM (Science, Technology

This paper reports perspectives of high school students and their teachers on how context-based learning can support students with varied educational and vocational aspirations to engage with and learn about emerging fields. Specifically, through analyses of qualitative data from student and teacher interviews, this study explores how a healthcare context-based curriculum featuring in-class activities and an out-of-class datathon can introduce students to core concepts, practices, and the role of fields like data science, AI, and ML. Suggested citation: A.D. Bopardikar, M. Cassidy, A. Gardiner

This paper presents an empirical study of high school teachers’ perspectives on context-based learning about Artificial Intelligence (AI) and data science. Four teachers were interviewed after they had enacted a curriculum contextualized in healthcare. The data were coded for teachers’ perspectives on what students learned; on the kinds of tasks that engaged students; and on the challenges and needs in teaching and learning about these fields. While context-based learning has the potential to promote students’ career awareness and appreciation of AI and data science, future research needs to

Student engagement is a key predictor of academic achievement and is closely linked to career awareness, interest, and preparedness. Measuring student engagement during STEM learning is challenging for teachers, given the dynamic and ever-changing nature of these learning environments. Even when engagement data can be collected, leveraging this information to refine and personalise instruction requires significant experience and time. To address this, we are developing Scoutlier EngagEd, a digital teaching assistant that embeds in existing Learning Management Systems (LMS) to automatically and