Science

Teach mathematics and science using materials for the weather-focused Community Collaborative Rain, Hail, & Snow Network project.

This article portrays how citizen science (CS) projects can be integrated into elementary classrooms to enhance students’ sensemaking skills and connect to real-world science problems. For the last several years, we have been involved in a study, Teacher Learning for Effective School-Based Citizen Science (TL4CS), that developed materials for elementary school teachers to engage their students in data collection, analysis, and interpretation for two existing CS projects: Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) and the Lost Ladybug Project (LLP).

Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores.

Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores.

The recent surge of artificial intelligence (AI) in science education has heightened interest among the NARST community—a curiosity about how technology can transform education that has lasted for decades. Founded in 1928, NARST is an international organization of thousands of members focused on improving science education through research. This growing interest is evidenced by the launch of the Research in Artificial Intelligence-Involved Science Education (RAISE) Research Interest Group in 2022 and the increasing number of AI-related studies presented at NARST conferences.

The disproportionate impacts of societal challenges (e.g., climate change, air and water pollution) on minoritized groups expose systemic injustices and compels STEM educators to reframe the role of STEM education in society. In this article, we describe traditional approaches, contemporary approaches, and our proposed future approach in science and STEM education with a focus on equity and justice. First, we begin with conceptual framing for equity and justice.