Summary
The rapid emergence of generative artificial intelligence (GenAI) has sparked both optimism and unease within education. For geography and the broader social sciences, GenAI tools such as ChatGPT promise efficiencies in curriculum design, data analysis, and feedback, yet they also produce errors, false citations, and cultural oversimplifications. The use of large language models poses ethical dilemmas that risk undermining core disciplinary practices in the social sciences. This session situates geography educators within this tension, emphasizing the need for AI and data literacy that is critical rather than uncritical. Drawing on recent research in geography and social studies education, I propose a framework of “error typologies” that reveal how GenAI can reshape knowledge production and interpretation of curricular content. The session will engage participants in considering ways to integrate AI that fosters critical inquiry and civic literacy, without outsourcing essential human judgment to machines. Rather than treating GenAI as a shortcut for assessment or content delivery, educators are invited to explore how it can serve as a tool for critique, revealing questions of bias, representation, and the implications for its use in the social studies classroom. Attendees will leave with some practical strategies for guiding classrooms to critically evaluate GenAI outputs and apply disciplinary thinking to AI-generated claims. Bring your questions and comments!
Session Focus
All Grade Levels| Curriculum and Instruction| Technology
Conference Room
Clark
Meet the Presenter
Samantha Serrano
