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Achieving the ambition of global science education reforms remains an ongoing challenge. Ideas from other STEM domains, however, could spur needed innovation in science education. The maker movement – or engaging in making – and computer science education – or learning computing – have proven rich contexts for STEM learning. This review analyses making and computing education research with primary-aged learners for insights on designing more meaningful science learning, an underlying goal of reforms.

We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed.

Global science education reform calls for developing student knowledge-in-use that applies the integrated knowledge of core ideas and scientific practices to make sense of phenomena or solve problems. Knowledge-in-use development requires a long-term, standards-aligned, coherent learning system, including curriculum and instruction, assessment, and professional learning. This paper addresses the challenge of transforming standards into classrooms for knowledge-in-use and presents an iterative design process for developing a coherent and standards-aligned learning system.

Three-dimensional learning progressions (3DLPs) have received wide-scale attention in depicting learning pathways that students travel as they progress toward knowledge-in-use in making sense of phenomena and solving problems. Using a design-based research approach, this study put efforts into developing a 3DLP of matter, interaction, and energy at the middle school level and presents the essential design principles for developing 3DLPs.

Uses of Artificial Intelligence in STEM Education explores the transformative role of AI in STEM learning. It covers challenges and opportunities, highlighting the balance between human instruction and technological tools. The book examines innovative AI applications such as automated grading, intelligent tutors, and adaptive learning systems, while also addressing AI's role in supporting diverse learners, including those with disabilities. Ethical considerations and the need for adaptive teaching strategies are discussed, emphasizing how AI can reshape STEM education.