The future workforce is being drastically reshaped by artificial intelligence (AI) technologies. The advancement in AI theories, algorithms and practices has not only created great demands for AI scientists, engineers, technicians and entrepreneurs, but also reformulated the nature of work in almost all industries. Importantly today's students must gain a fundamental understanding of AI in order to be prepared to enter the workforce of the future. This project will design a 12-lesson high school curriculum called StoryQ and associated teaching guides that will provide students with firsthand experience on how narrative modeling, one of the oldest fields in artificial intelligence, can be developed while working on their language arts writing projects. By integrating age-appropriate mathematics, language arts, and computing concepts, researchers will leverage advanced data exploration and text mining technologies, and employ research-based pedagogical approaches to help high school students learn basic concepts in machine learning and artificial intelligence. 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.
Led by a multidisciplinary team of learning technology and data experts, machine learning researchers, and experts in the integration of narrative modeling and mathematics learning, the project will focus on helping students envision their future careers as powered by artificial intelligence. The researchers will create and test StoryQ, a web-based text mining and narrative modeling platform, and develop, implement, and test narrative modeling with StoryQ curriculum. Students will learn to design, build, test, and iteratively improve machine learning models of narratives sourced both from student and teacher selected literature and from students’ own writings. Beginning with core narrative concepts, students will engage in development cycles that lead them to explore their own writing, annotate models by hand, observe a trained text mining model at work, become familiar with error analysis, and ultimately build their own AI model and evaluation process. The project broadens participation among youth from underrepresented and underserved populations by recruiting participants from two Massachusetts school districts with ethnically and economically diverse populations. To create broadly inclusive learning experiences, students from diverse backgrounds will write narratives to express their cultures and personalities as part of the learning activities. Research questions include (1) How can learning environments be designed to help students understand core AI concepts including the structures in unstructured data and the roles of human insight in the development of AI technologies? and (2) How can learning environments be designed to help students develop awareness and interest in careers that are centered on text mining practices or broadly powered by AI technologies? The project will use a design-based research design. The research team will carry out class observations and in-depth analysis of observational data, and draw design principles for building learning environments that cultivate future STEM and ICT workforce. The project’s success will be evaluated by a group of external evaluators who are experts in diversity and inclusion in AI, computing education, mathematics education, and language arts and literacy education. The outcomes of the project include the resulting web-delivered classroom-ready AI curriculum modules, a teaching guide and teacher resources for high schools, which will be disseminated to teachers and professional development groups. The StoryQ technology will be freely distributed.
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.
