abstract
- This study demonstrates the educational potential of integrating artificial intelligence (AI) with drone photogrammetry in architectural education through a participatory learning approach. Conducted within the course Investigacion y Proyecto Arquitectonico at Tecnologico de Monterrey, the research involved architecture students using advanced technologies to analyze segregated urban areas in Mexico. The methodology employed DJI Phantom 4 RTK drones equipped with high-resolution cameras to capture comprehensive aerial imagery, processing 747 images covering 0.195 km2 with 2.15 cm/pixel ground sampling distance. Data was processed using Pix4D software integrated with AI technologies including convolutional neural networks (CNNs), SuperPoint/SuperGlue networks, and PointNet++ for enhanced spatial analysis. Educational outcomes were measured using the Motivated Strategies for Learning Questionnaire (MSLQ), revealing high intrinsic motivation (M=6.57, SD=0.53) and extrinsic motivation (M=6.43, SD=0.79) among participating students. The study demonstrates that AI-enhanced drone photogrammetry not only improves technical competencies but also fosters critical thinking, data interpretation, and systems understanding essential for modern urban planning practice.