Exploring how machines learn is an area of study by the University of Rochester (UofR), which is a subfield of Artificial Intelligence (AI) that allows computers to learn in a clear and direct manner – a program to be used in high schools for students in K-12 STEM (Science, Technology, Engineering and Mathematics) classes to promote scientific literacy.
The National Science Foundation’s Division of Information and Intelligent Systems provided a two-year grant of $295,563 for the project.
In today’s technology AI is infused in our daily life. It is reported that 58 million new jobs will be created by the use of AI in 2023.
As a part of the National Science Foundation’s missions of promoting inclusion in next-generation STEM education and advancing K-12 AI literacy, the project would contribute to increasing national prosperity.
The question being faced is how can we prepare K-12 students for this approaching reality? In light of these important developments, recently NY state has addressed the issue by approving new Computer Science and Digital Fluency standards for K-12 schools that will be implemented in 2024. Currently, there is very little activity on AI for K–12 schools.
The UofR aims to fill this gap by introducing machine learning as a discovery tool for data-driven scientific inquiry (the in K-12 science. Scientific inquiry is defined as the process that results in building scientific knowledge.
1,000 high school students from historically marginalized communities in STEM will be reached throughout the research activities and engaged in outreach in collaboration with the University’s David T. Kearn Center for Leadership and Diversity and urban school districts. Additionally, close to a dozen STEM teachers will be recruited, who value computing, data-driven, and inquiry-based learning, to participate in the research activities.
The program will be in line with the cross-cutting concepts and scientific practices at the core of the New Next Generation Science Standards and the latest NYS Computer Science and Digital Fluency standards.
The following team of Rochester researchers will explore the unique learning opportunities of machine learning in promoting scientific inquiry: Zen Bai (Computer Science faculty) the principal investigator, co-principal investigators Michael Daley and Raffaella Borsai (Warner School of Education faculty) and Jiebo Luo (Computer Science faculty), along with senior associate Michael Occhino, Director of Science Education Outreach for the Warner School’s Center for Professional Development and Education Reform.
“Our goal for this project is to develop the tools and strategies that allow high school STEM classrooms to use machine learning as a common analysis tool, much like today’s calculator, to make sense of complex scientific phenomena and ask big questions ignited by thought-provoking patterns hidden in real-world data,” Daley said. “With Group-It, we will equip and support teachers in creating machine learning-powered scientific inquiry activities for their students. The project can make a broader impact by sharing our scientific research, ideas, and tools with educators in an easy, accessible, and understandable way.”
High school students and teachers with limited mathematical, programming, and data skills will be provided a novel programming-free, visual-based machine learning-powered learning environment called Group-It. This will allow them to learn basic machine learning concepts and methods and understand patterns hidden in multi-dimensional data in STEM contexts.
Group-It will utilize a combination of novel glyph-based (symbol based) data visualization and analogical learning process to decrease the deep learning curve of machine learning and multi-dimensional pattern discovery for high school learners.
New information from Group-It visual learning environment and machine learning-powered scientific inquiry activities will be publicly available on the project’s website. Teachers will have access to resources to help make the best use of these products and findings.
As a result of this project, the researchers predict that students will be prepared to address AI shortages, increase diversity in AI and promote a more scientific and AI literate society.