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From May 2014 through June 2019, educational, health, and academic partners under an NIH Science Education Partnership Award (SEPA) engaged 1271 6th through 8th grade students and their families in the “A New Genomic Framework for Schools and Communities” program. Evaluation addressed the effectiveness of the Health in Our Hands genomics curriculum, which employed Next Generation Science Standards and community action research projects to target two common, complex conditions—type 2 diabetes and substance use disorder (SUD)—in the underserved cities of Flint and Detroit, MI, USA.

This article provides an example of how career exploration can be embedded in an NGSS-aligned middle school unit to help increase student awareness, build interest in STEM careers, and introduce role models and mentors for students. Students develop science identity by doing science and studying topics that are relevant to their lives. Through these types of activities, they begin to see themselves as someone who understands and does science

Pages 3 - 11 in Michigan Science Teacher Association (MSTA) Winter 2021 Newsletter

Artificial intelligence (AI) incorporates the applications of machine-learning systems dominantly within the automated assessment and intelligent tutoring systems. These AI applications have promising potential to increase capacity within science, technology, engineering, and mathematics (STEM) education by supporting the social and cognitive development and learning experiences of students.

Artificial Intelligence (AI) technologies, including machine learning and data mining, have been extensively adopted to automate scoring of student responses to performance-based assessment tasks in classrooms, subsequently generating informative assessment reports. Although the innovative potential of these technologies is widely acknowledged, teachers often find it challenging to utilize these assessment data for instructional decisions.

Investigating AI's role in educational assessments, this study compares AI- provided and teacher scores of hand-drawn scientific models by Multilingual Language Learners (MLLs) in elementary classrooms. Using Convolutional Neural Networks (CNN) for scoring, we aligned AI assessments with those of experienced teachers. The results show moderate agreement (Kappa = 0.326), with AI favoring mid-range scores, while teachers provided a broader score spectrum. This suggests AI's consistency may miss the interpretive nuances teachers offer.

Haudek, K. C., & Zhai, X. (2024). Examining the effect of assessment construct characteristics on machine learning scoring of scientific argumentation. International Journal of Artificial Intelligence in Education. 34(4) https://doi.org/10.1007/s40593-023-00385-8