AI-Enhanced Personalization for Large-Scale University Education in Sustainability Science

Computer-based learning support systems commonly used in higher education provide instructors with the ability to post course materials for students to review. Even though the students in a typical class have different learning styles and interests, current learning support systems do not provide the instructor with the capability to customize course materials to meet the learning needs of individual students.

The goal of this project is to develop and evaluate an AI based learning support system that will automatically build models of students interests, goals, knowledge, and experiences. The system will then use these models to customize course content posted by the instructor to match the learning needs, and interests, of each student in the class. The resulting system will produce personalized educational material at scale, potentially improving student learning, and addressing equity in large university settings.

This project advances research in learning by building and evaluating a system that automatically accounts for a student’s learning style based on their individual backgrounds. This system will be developed and deployed in the context of two large undergraduate courses. The core intervention involves three main components: (1) the design and evaluation of a system that can interview students about their backgrounds; (2) transform these findings into a KG representation making its knowledge of the students available to both the instructor and the students; and (3) develop a system to personalize course content posted by the instructor.

Research conducted as part of this project will use both quantitative and qualitative methods providing a more nuanced understanding of students’ experiences with both the process and the personalized materials and will provide insights into how the systems and processes might be improved.

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.