Scholarly record
INTEGRATING ARTIFICIAL INTELLIGENCE IN GEOSCIENCE EDUCATION: OPPORTUNITIES, CHALLENGES, AND PEDAGOGICAL MODELS
Abstract
The rapid advancement of artificial intelligence (AI) is transforming the ways geosciences are taught, learned, and applied in both academic and professional environments. From Geographic Information Systems (GIS) and remote sensing to predictive environmental modeling, hazard assessment, climate analysis, and urban digital twins, AI has become an increasingly important component of geoscientific research and decision-making. As the green and digital transitions accelerate globally, higher education institutions are under growing pressure to modernize geoscience curricula and prepare graduates with the analytical, technological, and interdisciplinary competencies required for complex spatial problem-solving. In this context, integrating artificial intelligence into geoscience education represents both a strategic opportunity and a significant pedagogical challenge. This study explores the integration of AI in GeoScience Education by examining the opportunities, challenges, and pedagogical models associated with AI-enhanced teaching and learning. The research focuses on key domains such as GeoAI, machine learning, deep learning, spatial big data analytics, drone mapping, digital twins, sensor-based environmental monitoring, and intelligent urban systems. Particular attention is given to how AI can improve students’ spatial thinking, critical reasoning, predictive modeling skills, and data interpretation capacities across disciplines including geography, geology, environmental science, urban planning, and disaster risk management. Using an interdisciplinary methodological framework combining systematic literature review, comparative analysis of university curricula, case studies of smart laboratories and living labs, and policy evaluation of digital education strategies, the study identifies best practices for curriculum innovation and competency development. The analysis also examines the ethical, institutional, and infrastructural challenges of AI integration, including digital inequality, access to technological resources, data ethics, academic integrity, algorithmic bias, and the need for faculty training.
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References12
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