The Next Big Question: Adaptive, Inquiry-Driven AI Literacy for the 21st-Century Classroom

Artificial intelligence is rapidly changing the skills students need to be successful in work, learning, and civic life. At the same time, many college students outside computer science, engineering, and related STEM fields have limited access to evidence-based AI education that helps them understand how AI systems work, when AI tools can be useful, and when their outputs should be questioned. This project addresses that gap by developing and testing an adaptive AI literacy program for students and instructors across the humanities, social sciences, and related disciplines. The central question is how students can be prepared to move beyond simply using AI tools toward asking better questions about their assumptions, use-cases, limitations, reliability, and social consequences. The project advances the science of learning by studying how students develop the knowledge and judgment needed to work with AI responsibly and effectively. It will also produce broad and adaptive instructional materials paired with guidance to help colleges across the nation prepare graduates for an AI-embedded workforce while strengthening critical and ethical reasoning, alongside curating an informed public understanding of AI. The project contributes to the field of computer science by foregrounding the cognitive skills necessary for working with AI-augmented systems and developing collaboration technologies focusing on these skills. The project is centered around implementing and evaluating a two-part AI literacy framework. First, the research team will identify the AI-related concepts, skills, and ethical questions most important for students and instructors across disciplines using surveys, interviews, classroom observations, and stakeholder input. Second, the team will build and test adaptive instructional modules that adjust the level and type of support students receive as they learn. These modules will teach foundational AI concepts, support inquiry-based learning, and help students distinguish productive uses of AI from uses that may undermine learning. Throughout this, the team will deploy a range of research methods including pre- and post-assessments, learning analytics, mixed-methods analysis, soliciting student reflections and instructor feedback, and develop computational tools to identify evidence of reflective inquiry in student work. The project will culminate in a research-tested faculty toolkit, a set of open instructional resources, and validated evidence about how adaptive AI-supported learning can be implemented across disciplines in higher education settings. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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.