Improving Data and AI Literacy of Liberal Arts Students through Project-based Interventions in Liberal Arts Courses

The project aims to serve the national interest by infusing data and artificial intelligence (AI) literacy in liberal arts (LA) curricula. The curriculum interventions will specifically enhance LA students’ basic understanding of data analytics and AI methodologies. The interventions will also enable the LA students to understand the limitations and ethical implications of data analytics and AI methodologies when they are applied for data driven and autonomous decision-making in real-life environments.

The significance of the project is the transformation of LA curricula, which will prepare non-STEM students for current and future jobs, for which the capability of data analysis and knowledge extraction, along with critical understanding of AI's ability and limitation is vital. Overall, the project will build human capital by infusing data and AI literacy in the non-STEM workforce, enabling them to be productive members of today’s knowledge-driven workforce, regardless of their specific disciplinary training.

With an overarching goal of improving data and AI literacy (DAIL) among the students taking courses in LA disciplines, the research team will pursue the following four research activities. First, is to define data science and AI literacy for LA curricula. Second, is to investigate the alignment of AI and data science education across different liberal arts courses.

Third, is to design and develop AI and data science course projects and software tools by considering: (a) the existing course materials, the learning objectives, and the duration of a given course; (b) instructors’ and students’ ability and facility with AI and data science technologies; and (c) the long-term impact and broader understanding of the key concepts of AI and data science by the students. Fourth, and finally, is to design methods and metrics for independent assessment of the short- and long-term progress towards transforming LA education by improving the learning of AI and data science concepts by LA students.

The project’s deliverables will be learning materials, software, and publications, which will be disseminated through the project website and public access repositories. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.

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.