Everyday AI for Youth: Investigating Middle School Teacher Education, Classroom Implementation, and the Associated Student Learning Outcomes of an Innovative AI Curriculum
Everyday Artificial Intelligence for Youth (EdAI) addresses the need to develop a diverse workforce with the knowledge and skills to work with Artificial Intelligence (AI). The ubiquity of AI technologies in industry and in daily life calls for accessible and age-appropriate AI preparation of all learners. Broadening participation in AI is important in ensuring that AI technologies of the future are founded on principles of inclusivity and equitability. In this project researchers at Massachusetts Institute of Technology and Boston College will recruit and prepare 40 middle school teachers from school districts across Florida, Illinois, and Virginia. Through partnerships with these districts and four youth serving organizations, STEAM Ahead, Boston College’s College Bound, Supercomputing Challenge, and CodeVA, the project will engage over 1200 youths in AI education and foster their interest in AI intensive industries of the future. The majority of the youths are from Black and Latinx families. The project will be built upon the Developing AI Literacy (DAILy) curriculum that interweaves AI concepts, ethics in AI, and AI career awareness. The curriculum has been previously pilot tested among middle schoolers in a summer program. The EdAI professional development (PD) program will take a multi-pronged approach offering an AI Book Club, Practicum, Teacher Network, and Hackathon. Researchers will investigate how this PD model supports teachers to learn, adopt, modify, and teach the DAILy curriculum in a wide range of classroom settings and how the teachers’ implementation of the curriculum impacts students’ learning. This 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. In this project, four research questions will be investigated: 1): How can we best prepare a variety of teachers to use an innovative AI curriculum? What supports are necessary for teachers as learners and implementers of the curriculum? 2) What teaching practices are effective in supporting students’ learning of AI and related ethics and career issues? 3) What is the impact of variation in teaching practices and implementation settings on student learning? and 4) How and to what extent do teacher-led implementations of the DAILy curriculum impact student knowledge and interest in AI and AI-related careers? A design-based research approach will be employed to iteratively refine the teacher professional development program and the associated AI learning activities for both in-person and online contexts. The project will also develop and validate measurements and assessments of teachers’ perceptions of and attitudes towards AI, learning of AI concepts, and self-efficacy in teaching AI. The research will utilize a mixed methods design and collect quantitative data using attitudes toward AI surveys and AI knowledge and skills assessments from teachers and students as well as qualitative data including observations of teaching practices and interviews of teachers about their experiences of teaching AI. The findings will inform the AI education field of issues specific to expanding Black and Hispanic/Latinx participation in school-based AI education activities. The deliverables from the project include the EdAI program model, the teacher professional development program, the research findings on teachers’ learning and teaching of AI, and the effectiveness of the curriculum when implemented by middle school teachers in classrooms. 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.