Technology

Socioscientific issues (SSI) are problems involving the deliberate use of scientific topics that require students to engage in dialogue, discussion, and debate. The purpose of this project is to utilize issues that are personally meaningful and engaging to students, require the use of evidence-based reasoning, and provide a context for scientific information. This study highlights the value of integrating SSI in science education to engage students with social justice.

This paper explores the transformative impact of generative artificial intelligence (GenAI) on teachers’ roles and agencies in education, presenting a comprehensive framework that addresses teachers’ perceptions, knowledge, acceptance, and practices of GenAI.

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. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently.

Integrated STEM education (iSTEM) is recognized for its potential to improve students’ scientific and mathematical knowledge, as well as to nurture positive attitudes toward STEM, which are essential for motivating students to consider STEM-related careers. While prior studies have examined the relationship between specific iSTEM activities or curricula and changes in student attitudes, research is lacking on how the aspects of iSTEM are operationalized and their influence on shifts in student attitudes towards STEM, especially when considering the role of demographic factors. Addressing this gap, our study applied multilevel modeling to analyze how different iSTEM aspects and demographic variables predict changes in student attitudes.

Integrated STEM education (iSTEM) is recognized for its potential to improve students’ scientific and mathematical knowledge, as well as to nurture positive attitudes toward STEM, which are essential for motivating students to consider STEM-related careers. While prior studies have examined the relationship between specific iSTEM activities or curricula and changes in student attitudes, research is lacking on how the aspects of iSTEM are operationalized and their influence on shifts in student attitudes towards STEM, especially when considering the role of demographic factors. Addressing this gap, our study applied multilevel modeling to analyze how different iSTEM aspects and demographic variables predict changes in student attitudes.

Integrated STEM education (iSTEM) is recognized for its potential to improve students’ scientific and mathematical knowledge, as well as to nurture positive attitudes toward STEM, which are essential for motivating students to consider STEM-related careers. While prior studies have examined the relationship between specific iSTEM activities or curricula and changes in student attitudes, research is lacking on how the aspects of iSTEM are operationalized and their influence on shifts in student attitudes towards STEM, especially when considering the role of demographic factors. Addressing this gap, our study applied multilevel modeling to analyze how different iSTEM aspects and demographic variables predict changes in student attitudes.

Students benefit from dialogs about their explanations of complex scientific phenomena, and middle school science teachers cannot realistically provide all the guidance they need. We study ways to extend generative teacher–student dialogs to more students by using AI tools.

In this brief, The Potential of Using AI to Improve Student Learning in STEM: Now and in the Future, the authors examine the promise of AI as learning partner for students that offers personalized support and scaffolding.

In this CADRE brief, the authors Jeremy Price and Shuchi Grover explore the exciting possibilities and critical challenges of generative AI (GenAI) for STEM teaching. They examine how GenAI may shape STEM classrooms in the near future and identify promising trajectories for integrating GenAI into STEM teaching, including the potential for personalized teaching approaches. This brief delves into the current state of GenAI research and development around five dimensions of STEM teaching: Justice, equity, and inclusion; curricular decision-making and lesson planning; pedagogy and instruction; assessing student work and progress; and teacher learning and leadership.

Throughout the brief, the authors reaffirm the essential role of teachers and emphasize the need to include teachers in the design, development, and study of GenAI tools that are intended to benefit STEM education. They also highlight areas of critical concern, urging those involved in research and development to prioritize GenAI efforts in addressing the substantive problems of practice central to STEM teaching with an overarching aim to create more inclusive learning environments.

Teachers play a central role in designing structures which encourage the development of students’ individual creative capacity and the classroom’s sense of community. We offer considerations for designing engaging and collaborative experiences in elementary and intermediate computing education.