Project Profile

AI is driving our nation’s economic development and reshaping future jobs and workforce. K-12 education is facing the challenge of developing students’ AI competency and preparing them for the workforce of the future. Students need opportunities to engage in learning AI concepts that are situated in authentic AI content and experiences. This project will utilize image classification as an exemplary AI application domain to provide meaningful learning contexts for both high school students and teachers’ professional development. Researchers will partner with high school computer science teachers and recruit diverse high school students from five school districts in Mississippi that include tribal and rural schools with large, underrepresented minority student populations. A year-long out-of-school-time (OST) program with hands-on innovative AI technology experiences will be offered to engage high school teachers and students in preparing image data, training image models using machine learning (ML), and creating systems that can perform intelligent vision tasks. The project activities offer AI technology empowerment as well as identity-affirming spaces that aim to help diverse students develop both cognitive and non-cognitive skills, persist in college in the future, and potentially move on to careers in computing and artificial intelligence.

Fifteen high school teachers and sixty high school students will participate in innovative AI/ML education activities. The project team will explore the following research questions surrounding the AI/ML learning experiences and learning outcomes in the project: (1) What is the impact of the program experiences on students’ ML knowledge and competencies, as well as their interest and motivation to pursue AI or AI related careers? (2) How do students’ ML competency development trajectories evolve in the program and what are the factors shaping the trajectories? (3) What are the design characteristics that support accessible, equitable, and inclusive learning experiences for diverse high school students to develop ML competencies and AI ethics? (4) How do the program experiences affect high school teachers’ assumptions, values, and competencies of teaching ML from both an application perspective and an AI ethics perspective? Adopting the convergent parallel mixed methods research design, the project team will collect quantitative and qualitative data concurrently throughout the project. Quantitative data include student cognitive and affective learning outcome data, and qualitative data include student and teacher interviews, student group-work video recordings, and student artifacts. Both the quantitative and qualitative data will be longitudinal with repeated data collection during the year-long program for each cohort of high school teachers and students. One-way ANOVA, repeated measures ANOVA, and Friedman rank-sum tests will be used to analyze the quantitative data, and deductive and inductive thematic analysis and content analysis as well as matrix methods will be used to analyze the qualitative data. The outcomes of the project include the resulting high school AI/ML competency development curriculum and accompanying teaching guides and resources for computer science teachers, which will be made publicly available on the project’s website and disseminated to a large audience of STEM educators and researchers at conferences and publications.

This developing and testing innovations project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.