Collaborative Research: CER: REPS-AI: Supporting Students in Using GenAI in Responsible, Effective, and Pedagogically Sound Ways for Computational Coursework Across Disciplines

Generative artificial intelligence (GenAI) is changing how computer code is being written. Consequently, it is having dramatic impacts on education in computer science and other technological fields. But how will it impact the rest of campus? Many scholars across campus use coding in their work, so coursework for computational scientists, computational artists, and digital humanities scholars (among others) has always involved coding. These students will need different kinds of GenAI support than future professional software developers, because their tasks and their fields are different. This project's goal is to explore a range of tutoring-based models for teaching AI skills to non-computing majors, implemented at three different institutions of higher education. By comparing the effectiveness of these models, the project will both develop generalizable insights into AI tutoring methods and have direct impacts across a wide range of students in the U.S. The project is grounded in the observation that higher education is not equipped to support all students on campus in developing essential GenAI coding skills. The central goal of the project is to lay the intellectual and practical foundations to assist institutions in supporting all students in using GenAI tools in Responsible, Effective, and Pedagogically Sound (REPS-AI) ways to accomplish their computational coursework. Because different institutions will provide REPS-AI support in different ways based on their resources, structures, and strategies, this proposal will implement and evaluate three distinct approaches for providing campus-wide REPS-AI support to students at the three different schools. The three participating institutions range in size (two large, public, R1 land-grant institutions; one small liberal arts college) and hold differing visions for the role of GenAI on campus, ranging from early adoption/widespread integration to cautious and strategic approaches. Each campus also has differing existing infrastructure for providing REPS-AI support, resulting in three distinct strategies being pursued, including developing a new campus-wide student support center, integrating REPS-AI supports to existing student support centers, and creating mini-courses and workshops for students across campus. This project will produce three models of REPS-AI student supports, case studies of how to establish each on an IHE campus, and comparative analysis of the approaches. This in turn will allow other institutions to more quickly and confidently use the insights gained here to improve their own AI educational efforts. 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.