Real-Time AI-Driven Assessment and Scaffolding that Improves Students' Mathematical Modeling during Science Investigations

Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS) [1]. However, students often struggle at the intersection of these practices, i.e., developing mathematical models about scientific phenomena. In this paper, we present the design and initial classroom test of AI-scaffolded virtual labs that help students practice these competencies. The labs automatically assess fine-grained sub-components of students' mathematical modeling competencies based on the actions they take to build theirmathematical models within the labs. We describe how we leveraged underlying machine-learned and knowledge-engineered algorithms to trigger scaffolds, delivered proactively by a pedagogical agent, that address students' individual difficulties as they work. Results show that students who received automated scaffolds for a given practice on their first virtual lab improved on that practice for the next virtual lab on the same science topic in a different scenario (a near-transfer task). These findings suggest that real-time automated scaffolds based on fine-grained assessment data can help students improve on mathematical modeling.