AI Collaborative and Active Learning for GIS Course with School Bus Routing Project

Abstract

The rapid proliferation of generative artificial intelligence (AI) has necessitated a paradigm shift in higher education, particularly within Geographic Information Systems (GIS) pedagogy. Traditional models often fail to bridge the gap between abstract spatial theory and the technical frictions of real-world implementation. This study evaluates a Project-Based Learning (PBL) framework augmented by AI—acting as a "co-pilot"—to enhance student engagement and technical mastery. Centered on the Xinxing High School School Bus Routing Problem (SBRP), the project involved the high-precision scheduling of 1,124 students across 124 distinct demand nodes. By leveraging AI-augmented optimization, the research demonstrates significant operational dividends: fleet consolidation from 41 to 32 vehicles, a 19.3% reduction in carbon emissions, and the achievement of 100% punctuality. Furthermore, this collaborative approach addresses critical academic integrity concerns by transitioning from generic assessments to "Authentic Assessment," requiring localized data-driven reasoning and human-centric trade-offs.

Keywords: GIS Education, Project-Based Learning (PBL), School Bus Routing Problem (SBRP), Artificial Intelligence Collaborative Learning, Authentic Assessment

DOI: 10.54941/ahfe1008096