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

As educators pursue more authentic and relevant approaches to mathematics instruction, it is essential to understand how students reason mathematically in innovative learning environments. This study examines students’ reasoning with geometric transformations during a summer camp, in which they used dynamic geometry environments (DGEs) to design transforming, multi-functional products. Student artifacts were analyzed using two frameworks on reasoning in transformational geometry. taxonomy of transformational reasoning. Findings show that students’ reasoning was closely tied to their product

This paper reports on a five-day study exploring six middle school students’ developing understanding of algorithms and functions during the Building Algorithms challenge, an entrepreneurial Design & Pitch Challenge in STEM. Students proposed businesses and built automated spreadsheet algorithms to inform users’ decision making by assigning ratings to objects (e.g. videos, music, racetracks) based on their preferences. Part of a larger NSF study of students’ participation in entrepreneurial experiences with mathematical connections, the study used a design-based research methodology

This study investigates how teachers facilitate small-group mathematical discourse during Design & Pitch (D&P) Challenges in STEM, an experiential framework combining entrepreneurial, project-based, and design-based learning. By examining teacher interactions, we explore how educators draw on their personal practical knowledge to support student thinking and navigate the unpredictable nature of student-generated ideas in these innovative learning environments. This exploratory study investigates how teachers drew on their personal practical knowledge during group check-ins, leveraging teacher

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