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

High school science and biomedical pathway teachers need effective strategies to build student data literacy and prepare them to conduct experiments. This first article of two presents the data collection, visualization, and analysis lessons (Lessons 6–10) from the Data Science, AI and You (DSAIY) in Healthcare curriculum, co-developed and tested by teachers in diverse classroom settings using the Scoutlier online learning platform. The lessons guide students in collecting and visualizing data (Lessons 6–7) and in more advanced data interpretation, analysis, and application (Lessons 8–10)

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

There is a growing need in the United States for a workforce trained in quantum information science and technology (QIST), a disciplinary topic that is rarely addressed in precollege science, mathematics, and computer science curricula. University quantum physics and physics education researchers designed and initiated a 4-week, 12-h QIST professional development workshop for 𝑁=5⁢1 preservice and in-service secondary school science, mathematics, and computer science educators. A STEM integration framework guided the workshop structure, which incorporated a situated cognition model for learning

Quickly disseminating an innovative, timely afterschool program raises challenges, from recruitment and professional development to assessment, program fidelity, and quality. In this paper, we describe our experience as project developers, trainers, and researchers working with an afterschool network, Imagine Science, to disseminate a middle school club program about epidemic diseases and data. What we learned from working with this network may be useful to others who have created an afterschool science, technology, engineering, and mathematics (STEM) program they hope to spread widely

Despite the increasing number of women receiving bachelor’s degrees in computing (i.e., Computer Science, Computer Engineering, Information Technology, etc.), a closer look reveals that the percentage of Black women in computing has significantly dropped in recent years, highlighting the underrepresentation of Black women and its negative impact on broadening participation in the field of computing. The literature reveals that several K-16 interventions have been designed to increase the representation of Black women and girls in computing. Despite these best efforts, the needle seems to have

Background: Scholarship demonstrates that Black girls’ capacities to imagine possible futures in comput- ing are constrained by narratives of white masculinity and misogynoir embedded within computing. Building on race critical code studies and identity-as-narrative theories, we examine restorying through Black woma- nist storytelling methodologies for integrating Black girls’ intersectional identities when designing and reim- agining their computing futures. We ask: How might womanist storytelling methods support one Black girl in restorying possible computing futures? Methods: We present a