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2024 NSF ITEST PI Meeting Project Summaries

Welcome to the ITEST Project Summaries Library! These one page artifacts were created to share information and unpack project work across the three ITEST pillars 1) Innovative Use of Technology in Teaching and Learning, 2) Partnerships for Career and Workforce Preparation, and 3) Strategies for Equity in STEM Education.

We invite you to search, filter, and explore these pages—and add comments to engage with your colleagues! Feel free to also download the booklet.

Blue card with energy graphics to illustrate the "Energy Rapid Overview and Decision Support (En-ROADS)" computer model and simulation game
This project fosters STEM/ICT career knowledge through team-based, Integrated STEM learning experiences where high school students in urban and rural schools create climate solutions at global and local scales, supported by near-peer mentoring. After exploring sources and solutions to climate change, student teams play a “Climate Action Simulation” game, using a technology-rich digital platform and guides for a set of social roles. The game’s “engine” is an interactive global model, called “…
QuEST Summer Camp 2023
QuEST educates secondary school students in quantum science and computing activities while learning about career pathways in quantum technologies. Science teachers are also key stakeholders and attend professional development in quantum science instruction, quantum computing applications, and career pathways. QuEST, a partnership between Stony Brook University and the New York Hall of Science, advances quantum education, physical science literacy, and the diversity of the STEM pipeline through…
Semiconductors are essential components of electronic devices, enabling advances in all important applications and systems such as communication, healthcare, and national security. In order to sustain the U.S.’s global competitiveness in the semiconductor industry, there is a growing demand for skilled semiconductor workforce. High schoolers are among the most frequent users of electronic devices. However, many do not know how these devices are designed and manufactured. To address the…
Poster sharing highlights of TEAMAI project
TEAMAI is a collaboration between Indiana University (IU) Mathematics department and Looking Glass Ventures (LGV). In our research, we examine the high school teachers’ use of ALICE, an AI LLM module of LGV’s ‘Edfinity’ homework system (assessment platform at edfinity.com) in their Finite Mathematics and Calculus courses. Edfinity assessments utilize the, open-source WeBWorK format to deliver interactive, auto-gradable, isomorphic technology-enhanced assessments (TEAs) to support classroom…
student project
Our proposal introduces algorithm auditing to high school computer science classes. Algorithm auditing is a query method for understanding algorithmic systems’ opaque inner workings and external impacts from the outside. Sample student projects: (A) Drawing game using a ML classifier trained with accelerometer data; (B) Accelerometer sensor data for training and testing a ML model used in a sports game;
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In this project, we examined how gender, race, and ethnic heritage shape the STEM and higher education aspirations of different communities of refugee youth and families participating in a university-community organization partnership. Families from various ethnic-based community organizations in Arizona–serving Bhutanese, Burundian, Congolese, Somali, and Syrian people–participated in this qualitative study. Using social cognitive career theory as our conceptual framework and a qualitative…
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This project investigates how youth engage with algorithm auditing, a method that involves repeatedly querying AI/ML algorithmic systems and observing their output in order to draw conclusions about the system's opaque inner workings and possible external impact. We investigate how youth audit everyday ML applications. More specifically, (1) the feasibility of user-led algorithm audits by youth, (2) the dynamics of collaboration in algorithm audits, and (3) youth understanding of…
Chart showing inputs, AI learning experiences, and student and teacher outcomes
Project IntegrateAI seeks to engage learners in authentic, inquiry-driven projects to investigate scientific questions using natural language data. • For students, we engage them in innovative Natural Language Processing (NLP) learning experiences to foster their knowledge and skills in NLP and science, improve their attitudes toward STEM careers (interest, identity, and intention to persist), and enhance their ethical reasoning. • For teachers, we assist them in developing AI learning…
Two young Black students looking at a whiteboard covered with "Chatbot Brainstorming" written at the top. sticky notes, with
Project DIALOGS provides technology-rich learning opportunities for middle-school students to design and develop spoken conversational apps using computer science and artificial intelligence. Some 210 students from diverse, underserved schools with previously limited access to AI and computer science engaged in 2-week summer experiences to learn computer science and conversational AI development. Researchers in computer science and educational technology from the University of Florida…
High school student building a simple muscle computer interface
Relatively little research exists on the use of experiences with physiological sensors to support STEM education. In this work, we draw on techniques from physiological computing and computer science education to explore novel ways to build students' computational thinking skills. Learning barriers related to physiological expressions and physiological design may be less common with EMG-based (muscle) activities in comparison to EEG (brain) activities. Physiological design events seem to…